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The dataset generation failed
Error code: DatasetGenerationError
Exception: CastError
Message: Couldn't cast
task_id: string
model: string
error: string
prompt_tokens: int64
corpus: string
human_words: int64
released: string
cost: double
response: string
words: int64
output_id: string
origin: string
response_hash: string
item_id: string
max_tokens_budget: int64
reasoning_tokens: int64
family: string
reasoning: string
provider_pinned: string
attempts: int64
source: string
temperature: double
truncated: bool
completion_tokens: int64
finish_reason: string
provider: string
sample: int64
to
{'output_id': Value('string'), 'task_id': Value('string'), 'corpus': Value('string'), 'origin': Value('string'), 'family': Value('string'), 'released': Value('string'), 'model': Value('string'), 'response': Value('string'), 'words': Value('int64'), 'response_hash': Value('string'), 'finish_reason': Value('string'), 'truncated': Value('bool'), 'attempts': Value('int64'), 'cost': Value('float64'), 'item_id': Value('string'), 'source': Value('string'), 'reasoning': Value('string'), 'reasoning_tokens': Value('int64'), 'completion_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'provider': Value('string'), 'human_words': Value('int64'), 'provider_pinned': Value('string'), 'temperature': Value('float64'), 'max_tokens_budget': Value('int64'), 'sample': Value('int64')}
because column names don't match
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1835, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 621, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 361, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
task_id: string
model: string
error: string
prompt_tokens: int64
corpus: string
human_words: int64
released: string
cost: double
response: string
words: int64
output_id: string
origin: string
response_hash: string
item_id: string
max_tokens_budget: int64
reasoning_tokens: int64
family: string
reasoning: string
provider_pinned: string
attempts: int64
source: string
temperature: double
truncated: bool
completion_tokens: int64
finish_reason: string
provider: string
sample: int64
to
{'output_id': Value('string'), 'task_id': Value('string'), 'corpus': Value('string'), 'origin': Value('string'), 'family': Value('string'), 'released': Value('string'), 'model': Value('string'), 'response': Value('string'), 'words': Value('int64'), 'response_hash': Value('string'), 'finish_reason': Value('string'), 'truncated': Value('bool'), 'attempts': Value('int64'), 'cost': Value('float64'), 'item_id': Value('string'), 'source': Value('string'), 'reasoning': Value('string'), 'reasoning_tokens': Value('int64'), 'completion_tokens': Value('int64'), 'prompt_tokens': Value('int64'), 'provider': Value('string'), 'human_words': Value('int64'), 'provider_pinned': Value('string'), 'temperature': Value('float64'), 'max_tokens_budget': Value('int64'), 'sample': Value('int64')}
because column names don't match
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 956, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1887, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
output_id string | task_id string | corpus string | origin string | family string | released string | model string | response string | words int64 | response_hash string | finish_reason string | truncated bool | attempts int64 | cost float64 | item_id string | source string | reasoning string | reasoning_tokens int64 | completion_tokens int64 | prompt_tokens int64 | provider string | human_words int64 | provider_pinned string | temperature float64 | max_tokens_budget int64 | sample int64 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
asap-review:0003|human | asap-review:0003 | asap-review | human | human | null | human | This paper presents GibbsNet , a deep generative model formulated as transition operators . The transition operators are learned in an adversarial way , similar to that of the adversarially learned inference ( ALI ) . However instead of using a fixed prior p ( z ) , GibbsNet does not require the specification of a part... | 383 | 71b94bf7a55b036d | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0007|human | asap-review:0007 | asap-review | human | human | null | human | Summary : This paper discusses a method to train SPENs when strong supervision is not provided . Instead , training feedback comes in the form of a scalar-valued scoring function for a provided input as well as a prediction . The approach taken here is similar to that described in [ 1 ] in that score-violating pairs ar... | 412 | 355dae9f9b824908 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0009|human | asap-review:0009 | asap-review | human | human | null | human | The authors consider the setting of fairness in online learning . It is one of the initial works in the domain . The ideas used are not very novel but it is interesting as it is one of the first works in the domain . The authors prove an impossibility result to achieve fairness with equal false positive metric but show... | 237 | 4184e9fe9126caed | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0030|human | asap-review:0030 | asap-review | human | human | null | human | The authors propose a new semi-supervised boosting approach . The approach takes a set of supervised learning algorithms to simulate `` crowd-source `` labels of the unlabeled data , which are then used to generate a noisy label per unlabeled instance . The noise level is then estimated with an agreement-based scheme ,... | 660 | c00b23e9c78033ff | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0041|human | asap-review:0041 | asap-review | human | human | null | human | In this paper , the authors propose a physical driven architecture of DeepSFM to infer the structures from motion . Extensive experiments on various datasets show that the model achieves the state-of-the-art performance on both depth and pose estimation . In general , the paper is clearly written but I still have sever... | 177 | ed1326b996ac516f | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0048|human | asap-review:0048 | asap-review | human | human | null | human | This paper applies core-set selection to the training of GANs . The motivation is to limit the minibatch size with suitably sampled sets of datapoints . The proposed technique is relatively reasonable : e.g .extract features from an image , reduce dimensionality by the taking random projections , then run Core-Set sele... | 131 | a511edbbbde7fbb1 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0051|human | asap-review:0051 | asap-review | human | human | null | human | The paper presents a method for explaining the output of black box classification of images . The method generates gradual perturbation of outputs in response to gradually perturbed input queries . The rationale is that , by looking at these , humans can interpret the classification mechanics . The presentation is clea... | 187 | ef1cd8ea9396029a | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0066|human | asap-review:0066 | asap-review | human | human | null | human | The paper describes a new RL technique for high dimensional action spaces . It discretizes each dimension of the action space , but to avoid an exponential blowup , it selects the action for each dimension in sequence . This is an interesting approach . The paper reformulates the MDP with a high dimensional action spac... | 373 | b9250ecd88f129bc | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0071|human | asap-review:0071 | asap-review | human | human | null | human | In this paper authors propose a novel idea ( called Filter Summary , FS ) how to compress convolutional neural networks with 2D convolutions ( kernels are 3D tensors , it is also applicable to the 1D convolutions ) . Compression of the convolution operation is done with weight sharing : unwrapped kernel ( channel-major... | 709 | 9d3204bfe5c4323c | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0090|human | asap-review:0090 | asap-review | human | human | null | human | This paper show that Elman RNNs optimized with vanilla SGD can learn concepts where the target output at each position of the sequence is any function of the previous L inputs that can be encoded in a two-layer smooth neural network . There are multiple assumptions and complications in showing the main result . The cru... | 336 | 1e8f1cb961ee545a | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0093|human | asap-review:0093 | asap-review | human | human | null | human | This paper describes an improved training procedure for visual dialogue models . Rather than maximizing the likelihood of a collection of training captions , this approach first trains a discriminator model to rank captions in a given context by embedding them in a common space , then uses scores from this discriminato... | 542 | cfb5e54a1f2bbf94 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0111|human | asap-review:0111 | asap-review | human | human | null | human | This paper proposes a new approach to model based reinforcement learning and evaluates it on 3 ATARI games . The approach involves training a model that predicts a sequence of rewards and probabilities of losing a life given a context of frames and a sequence of actions . The controller samples random sequences of acti... | 512 | 2f255724389b6279 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0112|human | asap-review:0112 | asap-review | human | human | null | human | This paper proposed a neural attention model which has a learnable and differentiable sampling lattice . The work is well motivated as few previous work focus on learning the sampling lattice but with a fixed lattice . This work is quite similar to Spatial Transformer Networks ( Jaderberg 2015 ) , but the sampling latt... | 261 | 71d2a30186de116a | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0119|human | asap-review:0119 | asap-review | human | human | null | human | This paper targets modeling the temporal dynamics in high-dimension marked event sequences without any given causal network of markers . This is a difficult problem and rarely studies in previous studies . The authors propose a seminal adversarial imitation learning framework which consists of a generator model that ta... | 148 | 1a89001c1f594164 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
pmc-oa-parity:0001|human | pmc-oa-parity:0001 | pmc-oa-parity | human | human | null | human | Multiple testing:
The study compares 13 parameters (ALT, AST, glucose, cholesterol, albumin, urea, creatinine, Na, K, Cl, Ca, phosphate, Mg, plus 3 oxidative markers – effectively 16 tests) using independent t‑tests at α = 0.05.
the family‑wise error rate is ~56% (1 − 0.95^16). Many significant findings could be false ... | 283 | 01e5558637bdf4f6 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
pmc-oa-parity:0002|human | pmc-oa-parity:0002 | pmc-oa-parity | human | human | null | human | The manuscript entitled “Effects of Methandienone on the ovaries of rabbits in relation to the aromatase gene (CYP19A1), oxidative markers and the possible protective role of selenium” presents an interesting and scientifically relevant investigation.
Overall, the study is clearly written and well organized. The author... | 237 | e5df1defd1bab50d | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
pmc-oa-parity:0003|human | pmc-oa-parity:0003 | pmc-oa-parity | human | human | null | human | Background:
Hydatid disease is a widespread disease that affects all human organs.”
Comment 1:
This statement is overly general and scientifically inaccurate; hydatid disease primarily affects liver and lungs.
Aims:
This study aimed to evaluate antibodies (IgA, IgG, and IgM), complement proteins (C3 and C4), MIF of PMN... | 373 | 9487b0f91bc04e78 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
pmc-oa-parity:0004|human | pmc-oa-parity:0004 | pmc-oa-parity | human | human | null | human | I am satisfied that my comments have been addressed that the article is greatly improved.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Not applicable
Are all the source data underlying the resu... | 95 | b788ecce6545433c | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
pmc-oa-parity:0005|human | pmc-oa-parity:0005 | pmc-oa-parity | human | human | null | human | This article makes a significant contribution to the study of inclusive education through a comparative approach between Indonesia and Oman, two non-Western countries rarely studied side-by-side. The article's primary strength lies in its successful linking of socio-cultural contexts with educational governance structu... | 356 | c10a3505e18796e5 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
pmc-oa-parity:0007|human | pmc-oa-parity:0007 | pmc-oa-parity | human | human | null | human | The discussion is generally well-structured and connects findings to existing literature, but it occasionally makes interpretative jumps not fully supported by data, particularly regarding the support of the authors' findings. Nonetheless, the authors have used studies on child polpulation to support their data. This c... | 236 | fa27395450f4cd44 | stop | false | 0 | 0 | null | null | null | null | null | null | null | null | null | null | null | null |
asap-review:0041|openai/gpt-3.5-turbo | asap-review:0041 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, the paper presents a deep learning framework for Structure-from-Motion that explicitly enforces photometric consistency, geometric consistency, and camera motion constraints in the network. The proposed model, DeepSFM, outperforms traditional Bundle Adjustment and state-of-the-art deep learning methods for SfM... | 258 | 1244ab84941eb59a | stop | false | 1 | 0.004244 | asap-review:0041 | asap-review | 0 | 330 | 7,498 | OpenAI | 177 | openai | 0.7 | 3,000 | null | |
asap-review:0009|openai/gpt-3.5-turbo | asap-review:0009 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, the paper provides a comprehensive analysis of the interplay between sequential decision making and avoiding discrimination against protected groups in non-i.i.d. online settings. The introduction sets a strong foundation by highlighting the societal importance of addressing bias in machine learning applicatio... | 265 | c25d8ef26d006c59 | stop | false | 1 | 0.004706 | asap-review:0009 | asap-review | 0 | 318 | 8,457 | OpenAI | 237 | openai | 0.7 | 3,000 | null | |
asap-review:0003|openai/gpt-3.5-turbo | asap-review:0003 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, the paper presents a novel approach to learning the joint distribution between data and latent code using an adversarially learned iterative procedure. The authors introduce GibbsNet, a model that combines the advantages of directed and undirected latent variable models, achieving both speed and simplicity in ... | 329 | 8b6575fba304e263 | stop | false | 1 | 0.003734 | asap-review:0003 | asap-review | 0 | 395 | 6,283 | OpenAI | 383 | openai | 0.7 | 3,000 | null | |
asap-review:0030|openai/gpt-3.5-turbo | asap-review:0030 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | The paper "Boosting via Self Labelling: A Noise Resistant AdaBoost Algorithm" presents a novel framework for semi-supervised learning that aims to improve the performance of boosting algorithms on datasets with limited labelled data. The authors introduce a noise-resistant AdaBoost algorithm, combined with an inference... | 328 | b022b8af31c29731 | stop | false | 1 | 0.005484 | asap-review:0030 | asap-review | 0 | 407 | 9,747 | OpenAI | 660 | openai | 0.7 | 3,000 | null | |
asap-review:0007|openai/gpt-3.5-turbo | asap-review:0007 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | This paper presents a novel approach, SG-SPEN, which leverages truncated randomized search in reward functions to train structured prediction energy networks (SPENs) with efficient test-time inference. The paper addresses the problem of expensive labeling in structured output prediction tasks by using scalar reward fun... | 292 | c6c9d2b269337553 | stop | false | 1 | 0.003805 | asap-review:0007 | asap-review | 0 | 363 | 6,521 | OpenAI | 412 | openai | 0.7 | 3,000 | null | |
asap-review:0048|openai/gpt-3.5-turbo | asap-review:0048 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, this paper presents a novel technique for improving the training efficiency and performance of Generative Adversarial Networks (GANs) by using Core-set selection to mimic large batch sizes. The paper is well-written and provides clear explanations of the proposed method, the experiments conducted, and the resu... | 305 | 93021b0823b49232 | stop | false | 1 | 0.004033 | asap-review:0048 | asap-review | 0 | 363 | 6,976 | OpenAI | 131 | openai | 0.7 | 3,000 | null | |
asap-review:0093|openai/gpt-3.5-turbo | asap-review:0093 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, the paper presents a novel approach for training generative visual dialog models by transferring knowledge from discriminative models, resulting in improved performance in terms of diversity and informativeness of responses. The introduction provides a clear motivation for the work, highlighting the limitation... | 219 | dbf52037b9ef4d5b | stop | false | 1 | 0.00385 | asap-review:0093 | asap-review | 0 | 258 | 6,926 | OpenAI | 542 | openai | 0.7 | 3,000 | null | |
asap-review:0051|openai/gpt-3.5-turbo | asap-review:0051 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | The paper presents a novel method for explaining the outcomes of black-box classifiers, particularly in the context of medical image diagnosis. The method generates progressive variations of a query image that change the posterior probability of the classifier, providing insights into how the decision was made. The pap... | 251 | ad0f2fa50dedd0ee | stop | false | 1 | 0.004668 | asap-review:0051 | asap-review | 0 | 300 | 8,436 | OpenAI | 187 | openai | 0.7 | 3,000 | null | |
asap-review:0066|openai/gpt-3.5-turbo | asap-review:0066 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | The paper presents a novel approach to solving continuous control problems by leveraging sequential models and discretized action spaces. The authors introduce the Sequential DQN (SDQN) algorithm, which decomposes the original MDP into a hierarchical structure of Q functions, allowing for fine-grained discretization of... | 242 | e99d6dbe5b3a01ec | stop | false | 1 | 0.00476 | asap-review:0066 | asap-review | 0 | 294 | 8,637 | OpenAI | 373 | openai | 0.7 | 3,000 | null | |
asap-review:0111|openai/gpt-3.5-turbo | asap-review:0111 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | This paper presents a model-based approach to deep reinforcement learning, focusing on solving multiple tasks simultaneously. The authors introduce Predictive Reinforcement Learning (PRL) as an alternative to Deep Q-learning, separating the understanding of the environment from the strategy. The model consists of Perce... | 264 | a8b2f585fec5d9ae | stop | false | 1 | 0.002951 | asap-review:0111 | asap-review | 0 | 326 | 4,924 | OpenAI | 512 | openai | 0.7 | 3,000 | null | |
asap-review:0090|openai/gpt-3.5-turbo | asap-review:0090 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, this paper presents a novel theoretical analysis of Recurrent Neural Networks (RNNs) in terms of optimization and generalization simultaneously. The paper is well-structured, with clear definitions, notations, and problem formulations. The introduction provides a good background on RNNs and the challenges they... | 321 | c420ce828b9e1d24 | stop | false | 1 | 0.005373 | asap-review:0090 | asap-review | 0 | 384 | 9,593 | OpenAI | 336 | openai | 0.7 | 3,000 | null | |
asap-review:0112|openai/gpt-3.5-turbo | asap-review:0112 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | The paper "Neural Attention with Learnable Retinal Sampling Lattice" provides a detailed exploration of a neural attention model trained on a visual search task. The paper investigates the emergence of retinal sampling properties resembling those found in the primate retina and explores how these properties are influen... | 292 | 6aef78fded5fa662 | stop | false | 1 | 0.002628 | asap-review:0112 | asap-review | 0 | 343 | 4,226 | OpenAI | 261 | openai | 0.7 | 3,000 | null | |
pmc-oa-parity:0001|openai/gpt-3.5-turbo | pmc-oa-parity:0001 | pmc-oa-parity | model | openai | 2023-05 | openai/gpt-3.5-turbo | Review Report:
This study by Abed et al. investigated the physiological alterations in local Iraqi sheep affected by ruminal impaction by analyzing biochemical, electrolyte, and oxidative markers. The authors conducted a case-control study involving 20 adult female Awassi sheep, with 10 sheep diagnosed with ruminal im... | 204 | 2a46315eed97f3e4 | stop | false | 1 | 0.003387 | pmc-oa-parity:0001 | pmc-oa-parity | 0 | 265 | 5,978 | OpenAI | 283 | openai | 0.7 | 3,000 | null | |
asap-review:0071|openai/gpt-3.5-turbo | asap-review:0071 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | The paper presents a novel method for compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters called Filter Summary (FS). The proposed method reduces the number of parameters of each convolutional layer by learning a 1D vector termed Filter Summar... | 271 | 5789167aca51d1e4 | stop | false | 1 | 0.004906 | asap-review:0071 | asap-review | 0 | 339 | 8,794 | OpenAI | 709 | openai | 0.7 | 3,000 | null | |
asap-review:0119|openai/gpt-3.5-turbo | asap-review:0119 | asap-review | model | openai | 2023-05 | openai/gpt-3.5-turbo | Overall, this paper presents a novel approach for modeling latent dynamics in high-dimension marked event sequences. The proposed LANTERN framework addresses the challenges of unknown relation networks among markers and the intractable distance measures between event sequences. The paper is well-structured and provides... | 238 | e50e017a9348545b | stop | false | 1 | 0.003674 | asap-review:0119 | asap-review | 0 | 280 | 6,507 | OpenAI | 148 | openai | 0.7 | 3,000 | null | |
pmc-oa-parity:0002|openai/gpt-3.5-turbo | pmc-oa-parity:0002 | pmc-oa-parity | model | openai | 2023-05 | openai/gpt-3.5-turbo | Review:
The manuscript titled "Effects of Methandienone on the ovaries of rabbits in relation to the aromatase gene (CYP19A1) and oxidative markers and the possible protective role of selenium" by Shahooth et al. investigates the impact of methandienone on ovarian function in rabbits. The study explores the effects of ... | 246 | 3dbb2cdee23115db | stop | false | 1 | 0.003586 | pmc-oa-parity:0002 | pmc-oa-parity | 0 | 328 | 6,187 | OpenAI | 237 | openai | 0.7 | 3,000 | null | |
pmc-oa-parity:0007|openai/gpt-3.5-turbo | pmc-oa-parity:0007 | pmc-oa-parity | model | openai | 2023-05 | openai/gpt-3.5-turbo | Review Report:
This study titled "Oral Health Knowledge, Attitude, And Practices Among Residents Of Al-kharj, Saudi Arabia: A Cross-sectional Study" aims to assess the oral health knowledge, attitudes, and behaviors of adults in the Al-Kharj region of Saudi Arabia. The study collected data through a cross-sectional su... | 237 | 17e8d586a9d8d51c | stop | false | 1 | 0.005615 | pmc-oa-parity:0007 | pmc-oa-parity | 0 | 310 | 10,300 | OpenAI | 236 | openai | 0.7 | 3,000 | null | |
pmc-oa-parity:0003|openai/gpt-3.5-turbo | pmc-oa-parity:0003 | pmc-oa-parity | model | openai | 2023-05 | openai/gpt-3.5-turbo | Review:
This study by Hussain et al. aimed to evaluate the immunological effects of protoscolex antigen injection on hydatid cyst development in mice. The study included a well-described methodology involving the injection of protoscolex antigen in BALB/c mice, followed by measurements of various immunological paramet... | 307 | 25570c6203355c40 | stop | false | 1 | 0.00399 | pmc-oa-parity:0003 | pmc-oa-parity | 0 | 409 | 6,753 | OpenAI | 373 | openai | 0.7 | 3,000 | null | |
pmc-oa-parity:0005|openai/gpt-3.5-turbo | pmc-oa-parity:0005 | pmc-oa-parity | model | openai | 2023-05 | openai/gpt-3.5-turbo | Review:
This manuscript titled "Inclusive Education Implementation in Oman and Indonesia: A Comparative Analysis Study" by Siti Masyithoh et al. provides a detailed and comprehensive analysis of inclusive education policies and practices in Indonesia and Oman. The study employed qualitative document analysis of 33 pol... | 287 | 52fb257b2867b8dd | stop | false | 1 | 0.004746 | pmc-oa-parity:0005 | pmc-oa-parity | 0 | 362 | 8,405 | OpenAI | 356 | openai | 0.7 | 3,000 | null | |
pmc-oa-parity:0004|openai/gpt-3.5-turbo | pmc-oa-parity:0004 | pmc-oa-parity | model | openai | 2023-05 | openai/gpt-3.5-turbo | Review Report:
This manuscript presents a qualitative study that explores community perspectives on drug promotion on social media during the COVID-19 pandemic in KwaZulu-Natal, South Africa. The study used workshops to gather insights from young adults and healthcare providers on the risks and benefits of drug promot... | 350 | 09f4d9967e38853a | stop | false | 1 | 0.004192 | pmc-oa-parity:0004 | pmc-oa-parity | 0 | 424 | 7,112 | OpenAI | 95 | openai | 0.7 | 3,000 | null | |
asap-review:0009|openai/gpt-4o-2024-11-20 | asap-review:0009 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | ### Peer Review of the Submitted Paper
#### Summary
The paper addresses the important and timely topic of fairness in online learning, particularly in sequential decision-making tasks where examples arrive in a non-i.i.d. manner. The authors explore the interplay between adaptivity and group fairness, focusing on how ... | 1,016 | 805992c4052cab01 | stop | false | 1 | 0.034775 | asap-review:0009 | asap-review | 0 | 1,366 | 8,446 | OpenAI | 237 | openai | 0.7 | 16,384 | null | |
asap-review:0041|openai/gpt-4o-2024-11-20 | asap-review:0041 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | ### Peer Review for "DeepSFM: Structure-from-Motion with Deep Learning and Physical Constraints"
The paper titled *"DeepSFM: Structure-from-Motion with Deep Learning and Physical Constraints"* presents a novel deep learning-based framework for structure-from-motion (SfM). The authors propose an architecture that combi... | 1,064 | 11f7563cfca38f6b | stop | false | 1 | 0.0334 | asap-review:0041 | asap-review | 0 | 1,489 | 7,404 | OpenAI | 177 | openai | 0.7 | 16,384 | null | |
asap-review:0007|openai/gpt-4o-2024-11-20 | asap-review:0007 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | **Peer Review for "Search-Guided Training of Structured Prediction Energy Networks (SG-SPENs)"**
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### Summary
The manuscript introduces a novel method, Search-Guided Training of Structured Prediction Energy Networks (SG-SPENs), that uses light supervision in the form of a reward function to train SPENs without the... | 1,160 | 48c294103ef25138 | stop | false | 1 | 0.032158 | asap-review:0007 | asap-review | 0 | 1,604 | 6,447 | OpenAI | 412 | openai | 0.7 | 16,384 | null | |
asap-review:0003|openai/gpt-4o-2024-11-20 | asap-review:0003 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | **Peer Review for the Machine Learning Paper "GibbsNet: An Adversarially Learned Iterative Procedure for Deep Graphical Models"**
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### Summary
This paper introduces *GibbsNet*, a novel approach to learning joint distributions \( p(x, z) \) in deep graphical models that combines the strengths of directed and undir... | 1,099 | 7e039e507efdc25b | stop | false | 1 | 0.03108 | asap-review:0003 | asap-review | 0 | 1,541 | 6,268 | OpenAI | 383 | openai | 0.7 | 16,384 | null | |
asap-review:0030|openai/gpt-4o-2024-11-20 | asap-review:0030 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | ### Peer Review of "Boosting via Self Labelling (BSL)"
#### Summary
The paper proposes a novel semi-supervised learning framework, **Boosting via Self Labelling (BSL)**, designed to handle datasets with limited labelled data by generating noisy labels for unlabelled data and employing a noise-resistant variant of AdaB... | 1,018 | 2bdbcc1962315c41 | stop | false | 1 | 0.038015 | asap-review:0030 | asap-review | 0 | 1,406 | 9,582 | OpenAI | 660 | openai | 0.7 | 16,384 | null | |
asap-review:0071|openai/gpt-4o-2024-11-20 | asap-review:0071 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | ### Peer Review of the Submitted Paper on FSNet
#### Summary
The submitted paper introduces **Filter Summary Networks (FSNet)**, a novel method for compressing convolutional neural networks (CNNs) by leveraging a weight-sharing mechanism. FSNet represents convolutional filters as overlapping 1D segments within a compa... | 957 | 9fa54d7fcefc0555 | stop | false | 1 | 0.036073 | asap-review:0071 | asap-review | 0 | 1,413 | 8,777 | OpenAI | 709 | openai | 0.7 | 16,384 | null | |
asap-review:0051|openai/gpt-4o-2024-11-20 | asap-review:0051 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | **Peer Review of the Submitted Paper**
**Summary and Contribution:**
The paper addresses the critical need for interpretability in machine learning models, particularly in high-stakes applications like medical image diagnosis. It introduces a novel method for generating interpretable explanations by producing progre... | 977 | e7014bb2f96f6682 | stop | false | 1 | 0.03455 | asap-review:0051 | asap-review | 0 | 1,362 | 8,372 | OpenAI | 187 | openai | 0.7 | 16,384 | null | |
asap-review:0066|openai/gpt-4o-2024-11-20 | asap-review:0066 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | **Peer Review of Machine Learning Paper: Sequential DQN (SDQN) for Continuous Control Tasks**
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### Strengths:
1. **Novel Approach to High-Dimensional Action Spaces:**
- The paper presents a compelling method for tackling high-dimensional continuous control problems by discretizing action spaces and leveraging ... | 946 | c7cc71f4cc764f73 | stop | false | 1 | 0.034778 | asap-review:0066 | asap-review | 0 | 1,321 | 8,627 | OpenAI | 373 | openai | 0.7 | 16,384 | null | |
asap-review:0090|openai/gpt-4o-2024-11-20 | asap-review:0090 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | **Peer Review of "RNN Provably Learns the Concept Class"**
---
### Summary
This paper provides a novel theoretical analysis of Recurrent Neural Networks (RNNs), bridging the gap between optimization and generalization. The authors claim that RNNs, trained via vanilla Stochastic Gradient Descent (SGD), can efficiently... | 1,068 | 9fddca33be23b503 | stop | false | 1 | 0.038093 | asap-review:0090 | asap-review | 0 | 1,447 | 9,449 | OpenAI | 336 | openai | 0.7 | 16,384 | null | |
asap-review:0048|openai/gpt-4o-2024-11-20 | asap-review:0048 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | ### Peer Review of Submitted Paper
#### Summary
This paper introduces a novel method for effectively increasing the batch size in Generative Adversarial Network (GAN) training using Core-set selection. The authors propose a computationally efficient technique to approximate the benefits of large mini-batches without t... | 1,074 | 38591277ea935565 | stop | false | 1 | 0.032025 | asap-review:0048 | asap-review | 0 | 1,471 | 6,926 | OpenAI | 131 | openai | 0.7 | 16,384 | null | |
asap-review:0093|openai/gpt-4o-2024-11-20 | asap-review:0093 | asap-review | model | openai | 2024-11 | openai/gpt-4o-2024-11-20 | ### Peer Review for the Submitted Paper
#### Summary
The submitted paper proposes a novel training framework for generative neural sequence models in the context of visual dialog systems. It addresses the limitations of Maximum Likelihood Estimation (MLE)-based training, which often results in generic and disengaging ... | 956 | f43a8ab77bedb429 | stop | false | 1 | 0.031233 | asap-review:0093 | asap-review | 0 | 1,399 | 6,897 | OpenAI | 542 | openai | 0.7 | 16,384 | null |
End of preview.
Verbosity review materials (v1, 2026-10-24)
Peer reviews of 20 published papers written by humans (ASAP-Review: ICLR/NeurIPS 2016–2020; F1000Research, PMC open access) and by 41 model versions, plus model-judge extraction of substantive points, a prompt-only manipulation, and scripts.
- Browse:
verbosity-review-v1/(Chinese guide:verbosity-review-v1/README.md) - One-file download:
releases/verbosity-review-v1.zip
Human reviews keep the licenses of their sources (ASAP-Review, F1000Research). Point counts come from an LLM judge and were not checked by hand.
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