dataesr/scientific-paragraphs-categorization huggingface dataset:
HOMER: • The financial analysis appears to be intended to compare a proposed design with an existing base case with a pre-determined load. It was not readily apparent how novice or infrequent users could construct and compare economics using a null set (no generation and no load) as a base case. • Modeling of different rate tariffs is not straightforward in HOMER PRO. To accomplish this in the simulations for validation, it was necessary to treat it as if it were a negative annual O&M cost. While this procedure did result in valid cashflow analyses, it was cumbersome and required additional calculation steps outside of the software. It also prevented the direct use of this field to represent annual O&M expenditures. It is possible to model both financial aspects in this field simultaneously, but this would require conducting several sets of calculations outside of the model.
RETScreen allows users to conduct detailed financial analysis and project evaluation for their microgrid projects. This is important for ensuring the financial viability and sustainability of microgrid projects, as it allows users to identify potential financial risks and evaluate the economic benefits of different system configurations. Moreover, it also provides a range of pre-built templates and modules for different types of energy systems. Despite these limitations, the Toolkit offers several unique features and advantages that make it a valuable tool for microgrid design and optimization, particularly for low-income investors and users with limited technical expertise. One key advantage is that the Toolkit is completely free and opensource, making it accessible to a wide range of users who may not have the resources to invest in commercial tools such as HOMER or RETScreen.
Acknowledging the necessity for more accurate and reliable methods to investigate pearl rotation and its relationship with the final shape of the pearl, we took advantage of the field of deep learning, specifically deep neural networks (DNNs). These networks have become the standard approach for various classification tasks, largely due to their exceptional performance in image recognition challenges. This success can be attributed to the availability of extensive, well-annotated datasets like the ImageNet dataset  15  , as well as the use of transfer learning. Transfer learning enables the utilization of pre-trained neural network models to enhance performance on related tasks. In our study, we apply this technique to our data, evaluating the link between pearl rotation and its final shape.
This confirms the significant role of rotation in detecting aragonite deposits and, consequently, in monitoring the formation of a Tahitian pearl. We thus provide a first rotation database for the pearl, as well as a model to predict the final shape of the pearl from new rotation data. This non-invasive method of rotational tracking allows for the monitoring of pearl grafting and development without sacrificing the pearl oysters. It has potential applications in a variety of studies, including those focused on understanding the factors that influence pearl quality, optimizing pearl production in the pearl industry, and studying the mechanisms of pearl formation. By tracking pearl rotation and other characteristics during development, we may be able to gain new insights into the complex process of pearl formation and identify new ways to improve pearl quality.
Material and methods In this section, we present our complete methodology for studying pearl rotation. The first part of our approach involves the creation of magnetized nuclei, which are essential for our experiments. In the second part, we introduce our data acquisition device that allows us to collect high-quality data. To ensure accurate results, we describe our calibration process in the third part. The fourth part details the different grafts that have been made for our experiments. In the fifth part, we describe the process of data acquisition and processing, with dedicated software that we have developed. The classification model used to predict the final shape of the pearl from its rotation data over time is detailed in the sixth part. Overall, our methodology provides a comprehensive approach to studying pearl rotation and offers valuable insights into their behavior and demonstrates a direct link between the rotation patterns and the final shape of the pearl.
Therefore, a total of 52 oysters have been in our magnetometer device over a 1-year timespan. The information and final photos of the corresponding pearls are presented in Supplementary Fig. 1  and Table  2. Data processing. To process the colected data, software has been implemented in Matlab, available at https:// doi. org/ 10. 5281/ zenodo. 78720 14. The software takes raw data from the sensors as input and filters the noise using a Gaussian-weighted moving average filter. It then calculates and displays the orientation of the magnet over time. The orientation of the magnet at a given time is represented by a 3-dimensional coordinate (XYZ) in a space centered at the center of the pearl. To obtain each of the 3 coordinates, the values of each sensor are multiplied by the relative position of the sensors in the given space, and then summed (see Supplementary Table  3 ).
To transform our magnet rotation data into real rotation data of the pearl, a set of projections is needed. For a detailed description of the entire process, please refer to Supplementary Note 1. In addition to saving all the orientation data of the magnet and the pearl, images are captured to record the movement from the 3D visualization. Starting from the point of view that maximizes the visible rotation data, through a barycenter calculation, 6 images rotated by 60° are acquired for each acquisition (one pearl, 1 week). These images will then be used to classify the shape of the pearl. After classification tests, we found out that the first image taken on the 3D representation, which contains the most data, was sufficient, as the addition of the other images introduced noise. Thus, each sample in our dataset, which represents 1 week's data for one pearl, consists of a single RGB color image of size 224 × 224.
1%) To determine the amount of rotation data required to predict the final shape of a pearl with precision, we created several datasets in addition to the original one. One dataset comprised data from the last week before the oyster's sacrifice (with one sample per pearl), while another contained data from the last month before the oyster's sacrifice (with up to four samples per pearl). Additionally, we constructed a dataset from last week's data, with data separated by the day, to assess the predictive potential of rotation data over a short 24-h period. Each dataset distribution is described in Supplementary Table  4. Table  1. Graft survival after 1 month. The quality of the nuclei was determined by their irregularity compared to a standard nucleus. All nuclei were reviewed by 3 experts.
The grafts were carried out at the following pearl farms: (a) Tahiti Iti Pearl Farm (Teahupoo, Tahiti) (b) Harry's Pearl Farm (Apataki) (c) IFREMER (Vairao, Tahiti) by Josh, Kamoka Pearl. To train the model, we partitioned the datasets into train, validation, and test sets using the repeated holdout technique (n = 100). We carefully separated the data linked to individual pearls to ensure exclusivity to one set, thereby reducing overfitting risks. The class balance across splits was maintained, and we allocated 70% of the data to the train set, 15% to the validation set, and another 15% to the test set. We also carefully considered the disctinction between late and early harvesting of our pearls and made sure to maintain a balanced representation of different harvest dates in all three data splits.
Once the conditions were established, individuals were randomly assigned to the three splits, ensuring that each individual was appropriately represented in the test set. To predict the pearl shape using rotation data and account for the limited dataset size, we employed transfer learning with pre-trained neural networks. After evaluating multiple options, presented in the "Material and methods" section, we determined that the VGG-16 convolutional neural network architecture  16  was the bestsuited model for our task, owing to its balance between accuracy and execution time. Previous studies  19, 20  have consistently demonstrated the effectiveness of the VGG-16 model across a diverse range of tasks when compared to alternative architectures. The VGG-16 model is pre-loaded with weights from the ImageNet 15 dataset and used to extract features from the input images.
To use this model, we normalized and resized our datasets images to 224 × 224 × 3. The complete architecture of our VGG-16 model, along with examples from our images, is illustrated in Fig. 4. The architecture includes 18 weight layers and 5 max-pooling layers, each with various functions. A detailed explanation of these functions can be found in Supplementary Note 2. After the output of the last max-pooling layer, the output is flattened to add two specific features to each sample with specific weights: the number of days of pearl cultivation until the oyster was sacrificed and the number of days between grafting and rotation acquisition, as rotation is not uniformly distributed during pearl formation. Thanks to an ablation study, it was found that adding these two features resulted in an accuracy gain of approximately 10% for the first feature and 5% for the second.
Additional custom layers, including Dense and dropout layers, are then incorporated to prevent overfitting. Finally, the output layer with SoftMax activation is added to classify the pearls' shapes into three categories. The entire process, from data acquisition to final shape prediction with new data, is summarized in Fig. 5. Each step is elaborated further in Supplementary Note 3. We evaluated our model's performance on various datasets using the repeated holdout method with 100 splits. The model accuracy and the weighted-average F1-score were determined for all splits, with associated standard deviations. A grid search was conducted on the training and validation split to optimize the model's hyperparameters on each dataset and find the best model, and the final hyperparameters are reported Supplementary Table  5.
Results and discussion Our goal was to investigate the hypothesis that there exists a correlation between the rotational behavior of a pearl and its final shape. Specifically, we sought to predict the final class of a pearl based on its rotation. Machine learning: first classification approach. Initially, we conducted exploratory analysis using conventional machine learning techniques to classify our pearls based on the acquired data. To process the dataset, we calculated features for each sample that captured the pearl's velocity and acceleration over time. These features were then subjected to binning in order to reduce their overall number. After experimenting with various binning numbers, we determined that selecting 100 features per day was the optimal choice. We utilized the BioDiscML tool  22  to optimize and evaluate multiple models from our dataset, allowing for effective model comparison.
Additionally, we explored the application of LSTM algorithms, which are specifically designed for time-series data analysis, as a means of classification. The outcomes of both approaches are summarized in Supplementary Table  6 , highlighting accuracy levels ranging from 20. 3 to 51. 6%. All of the above calculations were exclusively performed using the complete dataset and data under matrix form. However, the classification results were significantly unsatisfactory, with the highest achieved accuracy being only 51. 6% using a random forest classification approach. As a result of these disappointing outcomes from the previous methods that relied on direct features, our attention shifted towards deep learning methods for image classification. Visual observations of the movement representation served as inspiration for this approach, as they suggested a potential correlation between rotation and form.
Deep learning: VGG-16 architecture and image classification. We subsequently directed our focus to the VGG-16 architecture and converted our data into the image format, as explained in the "Material and methods" section. Our final dataset consisted of 218 images, with each image depicting a week's worth of pearl rotation. We removed outliers from the dataset, and a total of 47 distinct pearls were included in the images, with each pearl having a varying number of samples. To gain insight into the image processing approach of the VGG-16 architecture, we present in Fig. 6  the results of feature extraction from our images, using the same examples as shown in Fig. 4. The figure displays examples of averaged feature maps for a subset of VGG-16 layers for our three different classes. A comprehensive description of each block is provided in Supplementary Note 4.
From the three displayed images, a clear pattern emerges where a random rotation indicates "Round" pearls, an axial rotation signifies "Atypical" pearls, and no rotation is associated "Other" pearls. These observations confirm the preliminary observations obtained by Gueguen et al. 13. However, the rotation patterns are more varied than these three categories suggest, and they are often difficult for an observer to classify. This highlights the pertinence of using a classification model. The evaluation of the model, as well as all the calculated metrics, are presented in Fig. 7. For the daily, weekly, monthly, and full datasets, we obtained accuracies of 47. 1%, 73. 4%, 70. 1%, and 81. 9%, respectively, over the test set and for the final pearl predictions. These results validate that there is a correlation between the pearl's rotation and its final shape, which can be observed even by analyzing the pearl's rotation data only from the last week before the oyster's sacrifice.
However, analyzing the rotation daily seems to be insufficient to make a prediction. The calculation of the macro-average F1-score was performed to verify that the prediction is correctly performed regardless of the predicted class and the potential imbalance according to the dataset. Values of 43. 1%, 68. 7%, 62. 7%, and 81% were obtained, for the daily, weekly, monthly, and full datasets, respectively. These high values, except for the 1-day dataset, confirm the high quality of our classification, regardless of the predicted class. The F1-scores for each class have been computed and are presented in Table  2. Despite the imbalance in the monthly and daily datasets, the individual F1 scores show consistency in the weekly and full datasets. These results suggest that our model effectively and accurately classifies all three classes when trained on the weekly or full dataset.
The model achieved the best accuracy when trained on the entire dataset collected over a 1-year period. Our findings confirm that obtaining rotation data throughout the entire pearl formation period improves pearl classification, despite the irregularity of the rotation, compared to using only the last rotation patterns from the final week. In addition, the results indicate that using only the last month of rotation leads to lower performance compared to using either the entire dataset or only the last week. Although obtaining rotation data from the entire pearl formation period yields the best prediction results, it is not practical to repeat such a lengthy data acquisition for future experiments. The model trained on the weekly dataset achieved an accuracy of 73. 4%, allowing shape predictions with just one week of rotation acquisition.
We are providing the model trained on our full dataset as a reference for future predictions, regardless of the duration of future acquisitions, and to achieve the best possible prediction. Our model was trained on the data of 47 distinct oysters, from 3 different grafts performed in 3 distinct locations, which reduces the risk of overfitting on a specific graft and should allow reliable predictions for different oysters and grafts. With an accuracy of 79. 5% for weekly classification and 81. 9% for bead classification with rotation data acquired over several weeks, our final model establishes a strong correlation between pearl rotation during its formation and its final shape. The provided model and software offer a reliable, turnkey solution for predicting pearl shape from newly acquired rotation data, which can handle input of any size.
Limitations. Our model currently has some limitations, including the requirement of magnetized nuclei to study rotation. The introduction of a magnet has a weak influence on the graft and final pearl, confirmed by the proportion of round pearls we get, similar to a standard graft  23  , of approximately 30%. Nevertheless, the manual production of these nuclei prevents large-scale studies. Additionally, our rotation measurements do not account for the movement of the oyster during the experiments. Distinguishing rotation along the magnet's axis from immobility is also challenging. Although our database was acquired over a year, it is small and limited to reliable data from 47 individuals. While a larger study could yield more reliable results, predicting the shape of more than three different classes of pearls would be costly and time-consuming.
Acknowledgements We thank  Victor Labrune  for his help in designing the rotation data processing software,  Pierre Lyonnard  for his help with the maintenance of the experiment room,  Didier Defay  ( Vega Industrie ) for his help on the electronic system of magnetic field data acquisition. A special thanks also to  Erwan Vigouroux  and  Thomas Lemaître , who allowed a complete acquisition 7 days a week, 24 h a day for a full year by ensuring the survival and the good condition of the oysters in our device. This work was supported and funded by the  PinctAdapt  project.
Bathymetric data pre-processing MBES, motion sensor and sound velocity data were loaded into QPS Qimera 2. 4. Binary inertial measurement unit (IMU) data recorded with the Applanix Wavemaster were reloaded to apply true heave calculation, which is only available in post-processed data. We then ray traced the soundings, with a sound velocity profile that was almost constant with depth, and roll calibrated our system using Qimera 2. 4. Our ellipsoidal height measurements from the RTK antenna had occasional drop-outs of a few minutes, which we interpolated in Qimera. Therefore, tidal reduction was successfully achieved by vertical referencing to mean sea level using the 2008 Earth Gravity Model  (Pavlis et al. , 2012) , and lines recorded on different days and tidal phases matched. Spurious echoes were then manually removed by human experts inspecting the data in 2D and 3D editors.
Processing of multidetect data With our prototype NORBIT iWBMS firmware, we acquired MD in the hope of detecting black corals extending into the water column. In the field, we set the sensitivity slider controlling the proprietary Norbit algorithm to report MD at a location known for black coral occurrence (Ridge A, Figure  2B ) and kept the setting constant for the following survey. The data were stored in the Seabat 7k (s7k) data format developed by Reson A/S. Unfortunately, it was confirmed by the manufacturer that the beta firmware reported incorrect intensity values for MD. The intensity values were not used in this study. The survey produced 27,898,462 soundings, which included 601,846 MD (2. 2%, raw MD shown in Figure  2C ). The MD were filtered to account for systematic and environmental misclassifications and artefacts.
Ground truthing The ground-truth dataset is composed of underwater video footage acquired on 6 sites: 3 sites by divers and 3 sites by dropcamera. Technical divers provided descriptions, photos and underwater video footage taken with a GoPro Hero 9 camera. To accurately locate the dive sites, technical divers with rebreathers left an Ultra Short Baseline (USBL) modem on the seafloor, which could be located to within a few metres from the survey vessel. Technical diving does not produce bubbles during the dive, which could interfere with the acoustic measurements. In addition to the video recorded by the divers, underwater photos were taken every second from the survey vessel at three sites by attaching a GoPro video camera (GoPro Hero 7) to a rope with extra weight at the end. The rope was lowered to the seabed (indicated by the decreasing tension of the rope).
" 3. I do not understand why you do not want to correct for multiple comparisons for your registered analyses. By definition, correcting for multiple hypothesis testing requires to know how many tests you will run. It is only possible with registered hypotheses (as we do not know what exploratory analyses you will do and how much of it you will report). You say in the design table that you do not need to correct because "no related analyses are conducted using this variable". Well, you look at the same dependent variable in three hypotheses (H1, H2, H3), with the same dataset. In my view, these hypotheses are part of the same family of hypotheses (the OXT has an effect), which would justify correction. Thank you for this important comment. We indeed, incorrectly, stated in the design table that no related analyses are conducted using this variable, which we have corrected.
Importantly, however, we do not have reliable prior data to conduct an informed power simulation for the interaction between oxytocin treatment and trait scores: only a recent replication of Declerck et al. (2020) has looked at interactions with trust propensity, and there are no well-powered datasets for interactions with reward or punishment sensitivity. Nevertheless, the power simulation for H2 and H3, as you rightly indicate, remains interesting. In response to your comment, we have therefore adopted the following approach. Because we cannot conduct this power simulation (especially for H3) in a well-informed manner, we have decided to conduct the H2 and H3 power analysis as a "power spectrum analysis", in line with previous work from Quintana (2022). That is, rather than selecting a poorly-informed effect size that represents the (treatment by trait score) interaction, we simulate the power for a range of interaction effect sizes (using our alpha of 0.
We have now added the description and results of the power spectrum analysis in the Participants section and added a matrix ("heatmap") that summarizes the result of the power spectrum analysis to the manuscript. The text now reads: "As opposed to the power simulation for the main hypothesis, the power simulation for the treatment-by-trait score interaction hypotheses is more challenging, primarily because we do not have reliable priors for data simulation. The only well-powered study to date that examined an interaction between OXT (vs. placebo) and trust propensity on investments is the recent replication by Declerck and colleagues  4. Moreover, to our knowledge, there are no well-powered datasets to conduct an informed power analysis for interactions with sensitivity to reward and punishment.
Next, we simulated 1000 random datasets to derive our power for each unique combination of a difference in the treatment effect (odds ratio of 1=very small, odds ratio of 6=very large) for the low and high score trait groups. With alpha set to. 02, we obtain the power spectrum results as presented in Figure  3. As expected, this analysis demonstrates that the power to detect a significant interaction depends on the magnitude of the difference of the treatment effect for individuals who score high, compared to individuals who score low, on the trait. With our planned sample size of N=220, only large differences in the odds ratio would yield sufficient power to detect a treatment-by-trait interaction on investments. Importantly, this is not an entirely unrealistic situation: Let us assume that approximately 50% of individuals score low and 50% score high on a trait and take into account that Declerck et al.