dataesr/scientific-paragraphs-categorization huggingface dataset:
However, for sake of simplicity, in Table  4 , we show the results for th+ = 0. 5 and th-= 0. 9, as this configuration achieved the best results. Table  3. TaP for threshold model using only baseline features: Tf-idf (TI) and latent Dirichlet allocation (LDA). The first column corresponds to features, the second column to positive threshold, and the third to negative threshold. Results are grouped by datasets (i. e. , Instagram and Vine), and each column corresponds to one machine learning model. Best score for each model is underlined and best score for each dataset is highlighted in bold. In this case, for the Instagram dataset, the best TaP outcome is 0. 7561 when just Tf-idf are used as positive features, no matter the combination of negative features selected for the naïve Bayes model.
Doc2Vec Features In this first set of experiments, we evaluated whether Doc2Vec features can significantly improve performance. In all the experiments, sentiment and profane analysis features were included, and different combinations of syntactic and semantic features (i. e. , Tf-idf, LDA, and Doc2Vec) were tested. Figure  3a, b  show the results for the fixed early detection model at points 1, 5, 10, 15, and 25 for the Instagram and Vine datasets, respectively. The X-axis represents the syntactic and semantic features extracted from the posts. The first three items correspond to standard features (i. e. , Tf-idf, LDA, and the combination of both), which constitute our baseline. The remaining items correspond to Doc2Vec and its combination with Tf-idf and LDA features. From Figure  3a , we observe how the use of Doc2Vec features (stand-alone or in combination) improves performance, especially for the logistic regression and SVM models, and independently from the point when the fixed model makes the decision.
This behaviour is clearer on the Vine dataset (Figure  3b ), as there is a higher improvement for all machine learning models, except naïve Bayes. Introducing Doc2Vec features immediately improves performance, but the best-performing model on the Instagram dataset is SV M 10 , combining Doc2Vec with LDA and reaching 0. 529 (versus previous 0. 3755 value), while for Vine, the use of Doc2Vec reaches up to 0. 5716 with SV M (versus 0. 3794 previously obtained) also at point 10. Following this, we replicated the same experiments for the threshold early detection model. Figure  4a, b  show the results for both datasets. Since this model requires positive and negative threshold values, we show different line types for different thresholds. We limited our results to low positive thresholds (i. e.
, 0. 5 and 0. 6) and high negative thresholds (i. e. , 0. 8 and 0. 9), since they have provided good results in previous works  [6]. The rationale behind these values is clear: to provide an easy-to-surpass threshold for the early detection of cyberbullying cases with little information, while negative cases require a higher degree of confidence. From the figures, we observe a similar behaviour as in the previous case, with an important performance improvement. By introducing Doc2Vec, in combination with the other features, the performance significantly improves, except in the case of naïve Bayes and extra tree, where this difference is lower. With this model, on the Instagram dataset, that improvement is only clearly present for the th+ = 0. 6 and th-= 0. 8 configuration, while on Vine, a slight increase in the results is present for th+ = 0.
Each column represents the results for one machine learning model, while rows correspond to positive features. Since the best scores were achieved using Doc2Vec as positive features, we limited the rows to those cases. As in the previous figures, negative features are represented on the X-axis, and different values for positive and negative thresholds are represented on each graph. Focusing on the Instagram dataset (Figure  5 ), the best-performing models are Ad-aBoost and naïve Bayes, and th-= 0. 9 tends to achieve better results in most cases. The best score is 0. 7585, which corresponds to naïve Bayes using Doc2Vec and Tf-idf as positive features for all negative feature combinations with a low positive threshold (th+ = 0. 5). With respect to the Vine dataset (Figure  5b ), we observed a limited impact on all machine learning models for some feature combinations, both positive and negative.
Just low negative thresholds (th-= 0. 8) present a notable variation for logistic regression model. In the rest of the cases, the performance remains quite stable. AdaBoost and extra trees achieve good results in general and present a consistent behaviour, independently of positive and negative thresholds. In fact, the best score (0. 7167) is obtained by AdaBoost, using all features as negative and Doc2Vec and LDA as positive, with th+ = 0. 5 and th-= 0. 9. This result significantly improves the dual model baseline performance for the Vine dataset. I + L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I + L D A LR T I L D A T I + L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I + L D A 0. 5 0. 0 0. 5 NB T I L D A T I + L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I + L D A.
8 th+ 0. 6 th-0. 9  I L D A T I+ L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I+ L D A 1 0 ADA. D2V+TI+LDA T I L D A T I+ L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I+ L D A ET. D2V+TI+LDA T I L D A T I+ L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I+ L D A LR. D2V+TI+LDA T I L D A T I+ L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I+ L D A NB. D2V+TI+LDA T I L D A T I+ L D A D 2 V D 2 V + T I D 2 V + L D A D 2 V + T I+ L D A SVM. D2V+TI+LDA Features negative model TaP th+ 0. 6 th-0. 8 th+ 0. 5 th-0. 9 th+ 0. 5 th-0. 8 th+ 0. 6 th-0. 9 Table  5  summarises the main results for this first set of experiments, comprising the inclusion of Doc2Vec features. We observe that its incorporation significantly improves the results obtained by the early detection models for both datasets.
Interestingly, the performance improvement is more important on the Vine dataset. We consider that this may be motivated by the smaller post sizes (in terms of word count) and the limitations of Tf-idf and LDA to extract valid features, while Doc2Vec is able to better capture the information available. Regarding the machine learning models, naïve Bayes for the Instagram dataset and AdaBoost for Vine provide the best results. Another interesting point is the fact that the threshold and dual models significantly improve performance over the fixed model for all cases, except for the threshold model on the Vine dataset. However, there is no significant statistical difference in performance between them. In the remaining experiments, we focus on the threshold early detection model, since it requires a simpler configuration, and the performance results can be considered equivalent to the dual model.
In all our experiments, we ran models with the combination of all features defined: profane words, sentiment analysis, Tf-idf, LDA and Doc2Vec. Figure  6a   Regarding the Instagram dataset (Figure  6a ), the use of MIL, in many cases, reduces the performance of the model. There are some slight improvements for all models (except logistic regression) at the higher points. In fact, the best score using MIL is obtained by SVM at point 10 using the minimum as aggregation function, and corresponds to 0. 3384. However, there is no statistical difference in comparison with the fixed model without MIL, and it is significantly worse than the threshold and dual models. However, when considering the results for the Vine dataset (Figure  6b ), there is a significant change, especially for the AdaBoost and extra trees models, with MIL models providing major improvements.
The best score in this case is 0. 7926, achieved by AdaBoost at point 10 using the arithmetic mean, which is significantly better than the results without MIL for the fixed, threshold, and dual models. We consider that this important performance improvement by using multiple instance learning for the Vine dataset is directly related to the reduced post sizes on this social network and the lower use of standard English. On average, each post is composed of about 5 words, which provides limited information to be extracted by the different syntactic and semantic features for each of them. However, the use of a simple aggregation function (e. g. , average) for a number of posts allows for better combination of the information from the different posts than the concatenation of the posts themselves.
5, 0. 6, 0. 7, 0. 8, and 0. 9. Figure  7a, b    The behaviour on the Instagram dataset (Figure  7a ), as expected, is below the results achieved by the baselines provided in Section 4. 4. 2 for the fixed, threshold, and dual models (being significantly worse for the last two). Increasing the negative threshold improves performance, and the median aggregation function is consistently providing the best results in all cases, but there is no significant improvement in TaP results for this dataset. Regarding the Vine dataset (Figure  7b ), we observe how AdaBoost provides a better performance than extra trees. As in the previous case, increasing the negative threshold improves performance, while for the positive threshold, the best values are obtained with 0. 5 and 0. 6. This is a meaningful change with respect to the standard threshold model, motivated by the aggregation of information from multiple posts, which produces an increase in the class probability for the cyberbullying cases detected.
Results and Discussion All experiments were performed with two different social network datasets and evaluated under an early detection metric. This allowed for a proper evaluation from the early detection's point of view, as the delay is taken into account in the evaluation. In addition, the experiments were performed on datasets of different natures, as can be seen in the outcome of the analysis. They were performed solely with textual information, disregarding platform-dependent information. This draws attention to the syntactic and semantic features extracted from posts. Considering as a starting point the early detection models and features from our previous work  [6] , we have proven how Doc2Vec features significantly improve performance for all early detection models, with threshold and dual performing similarly.
The best TaP scores achieved for the Instagram and Vine datasets were, respectively, 0. 7585 and 0. 7167, improving the best respective baseline models in both cases. We have also shown how the introduction of multiple instance learning in early detection models has an important positive effect on the Vine dataset results, motivated by the smaller post sizes and lower use of standard English language, while there is no significant improvement on Instagram. The threshold model is able to reach a score of 0. 8149 with the AdaBoost model for the Vine dataset. In particular, in the Doc2Vec features section (Section 4. 4. 2), we assess the performance improvement for all early detection models. We find that it provides an important performance enhancement for threshold and dual models. As there is no significant statistical difference between them, the latter would be preferable due to its simpler configuration.
Conclusions and Future Work In this work, we have explored the cyberbullying early detection problem on social networks from a generic perspective, focusing only on users' comments. To summarise, the inclusion of Doc2Vec features improves all early detection models' performance, in particular threshold and dual. Moreover, multiple instance learning has shown better results for the Vine dataset, with smaller post sizes and lower use of the English language. In future works, we expect to continue exploring multiple instance learning by using more advanced instance-space MIL models, such as expectation-maximisation diverse density (EMDD), and also considering bag-space and embedded-space models, such as normalised set kernel (NST-SVM) or multiple instance learning via embedded instance selection (MILES).
Funding: This research was supported by the  Ministry of Economy and Competitiveness of Spain  and  FEDER  funds of the  European Union  (Project  PID2019-111388GB-I00 ) and by the  Centro de Investigación de Galicia "CITIC ", funded by  Xunta de Galicia  and the  European Union  ( European Regional Development Fund -Galicia 2014-2020 Program ), by grant  ED431G 2019/01.
(b) Vine Figure 7. 7 TaP for threshold early detection model based using MIL for Instagram (a) and Vine (b) datasets. Rows correspond to machine learning models: ADA and ET. Columns correspond to positive thresholds. The X-axis represents negative thresholds. Different aggregation functions are represented on each graph: minimum (Min), maximum (Max), average maximum and minimum (MeanMaxMin), arithmetic mean (Arithmetic), and median (Median).
Here we describe LegacyClimate 1. 0, a dataset of the reconstruction of the mean July temperature (T July ), mean annual temperature (T ann ), and annual precipitation (P ann ) from 2594 fossil pollen records from the Northern Hemisphere, spanning the entire Holocene, with some records reaching back to the Last Glacial Period. Two reconstruction methods, the modern analog technique (MAT) and weighted averaging partial least squares regression (WA-PLS), reveal similar results regarding spatial and temporal patterns. To reduce the impact of precipitation on temperature reconstruction, and vice versa, we also provide reconstructions using tailored modern pollen data, limiting the range of the corresponding other climate variables. We assess the reliability of the reconstructions, using information from the spatial distributions of the root mean squared error in the prediction and reconstruction significance tests.
The dataset is beneficial for synthesis studies of proxybased reconstructions and to evaluate the output of climate models and thus help to improve the models themselves. We provide our compilation of reconstructed T July , T ann , and P ann as open-access datasets at PANGAEA (https://doi. org/10. 1594/PANGAEA. 930512; Herzschuh et al. , 2023a). The R code for the reconstructions is provided at Zenodo (https://doi. org/10. 5281/zenodo. 7887565; Herzschuh et al. , 2023b), including the harmonized open-access modern and fossil datasets used for the reconstructions, so that customized reconstructions can be easily established.
Introduction The comparison of climate model outputs with climate data is essential for model improvements  (Eyring et al. , 2019). The extratropical Northern Hemisphere is of particular interest because it is known for complex spatial and temporal temperature and precipitation patterns. However, the period for which instrumental observations are available is only of limited use to validate simulations, in particular when assessing the climate response to natural climate drivers, because it is too short and because it is impacted by human-induced greenhouse gas forcing. Climate proxy data derived from natural archives are therefore of great value. Previous proxy-based climate inferences have contributed to major debates about Holocene climate change. For example, while simulations indicate gradual warming of the Holocene, temperature proxy data syntheses rather support a mid-Holocene optimum, which resulted in the "Holocene conundrum" debate  (Liu et al.
All these types of evaluations require a specific handling of the proxy data and have to be considered for proxy-model comparisons. Fossil pollen records are well established in their use as a paleoecological and paleoclimatological proxy and are of great value as indicators of past environmental and climatic change over many decades. Considerable efforts have been made to establish regional, continental, and even global data repositories like the North American Pollen Database (NAPD; https:// www. ncei. noaa. gov/products/paleoclimatology, last access: 1 July 2020), the European Pollen Database (EPD; http: //www. europeanpollendatabase. net/index. php, last access: 1 July 2020), and the Neotoma Paleoecology Database (https://www. neotomadb. org/, last access: 1 April 2021;  Williams et al.
Hence, designing a robust calibration dataset from modern pollen assemblages is a crucial part of the reconstruction process. A suitable calibration dataset should cover a wide range of climatic and environmental gradients in order to represent an empirical relationship between pollen assemblages and climate  (Birks et al. , 2010; Chevalier et al. , 2020). As with fossil pollen records, data syntheses and repositories also exist for modern surface pollen data, e. g. , for North America  (Whitmore et al. , 2005) , Eurasia  (Davis et al. , 2013 (Davis et al. , , 2020)) , and China  (Cao et al. , 2013; Herzschuh et al. , 2019). For temperature reconstruction time series, several broadscale syntheses exist; however, they either originate from different proxies  (Kaufman et al. , 2020a, b)  or are restricted to certain continents or regions and/or are poorly documented  (Mauri et al.
, 2015; Marsicek et al. , 2018; Routson et al. , 2019). Temperature reconstructions from extratropical Asia are mostly lacking. Precipitation syntheses are available from Europe  (Mauri et al. , 2015) , North America  (Gajewski et al. , 2000) , and China and Mongolia  (Herzschuh et al. , 2019) , but hitherto, no global or hemispheric syntheses of quantitative precipitation changes are available for the Holocene. In a recent effort, we synthesized and taxonomically harmonized pollen records available in the Neotoma Paleoecology Database  (Williams et al. , 2018)  and additional records from China and Siberia  (Cao et al. , 2013 (Cao et al. , , 2020) )  into a global late Quaternary fossil pollen dataset (LegacyPollen 1. 0;  Herzschuh et al. , 2022b)  and revised all chronologies of those records using a Bayesian approach that allows for the inference of temporal uncertainties (LegacyAge 1.
Input data The objective of this study is to create a dataset of quantitative reconstructions of T July , T ann , and P ann , spanning the last 30 kyr and beyond from fossil pollen records. These variables (or variables highly correlated to them) were shown to explain most variance in the modern pollen data (T July ; P ann ) or are typically used in syntheses and proxy-model comparison studies (T ann ). Accordingly, we selected these three variables. We used the fossil dataset compiled in Lega-cyPollen 1. 0 (stored on the PANGAEA open-data repository and presented in  Herzschuh et al. , 2022b ) that integrates pollen records archived in the Neotoma Paleoecology Database, a dataset from eastern and central Asia  (Cao et al. , 2013; Herzschuh et al. , 2019) , and a dataset from northern Asia  (Cao et al.
, 2020). Ages were taken from the Bacon  (Blaauw and Christen, 2011)  age-depth models presented in  Li et al. (2022;  LegacyAge 1. 0), and for each record, we provide an ensemble of 1000 realizations of the agedepth model in our data product so that it can be used to account for chronological uncertainty in the reconstructions. As the chronological and reconstruction errors are independent, they can be added in quadrature to obtain the combined error. With this information, users can easily produce curves with all relevant uncertainties (as shown in Appendix Fig. 1 ). We compiled the fossil data into four subcontinental datasets for eastern North America (< 104 • W;  Williams et al. , 2000) , western North America, Europe (< 43 • E), and Asia (> 43 • E). We restricted the analyses to the 70 most common taxa on each continent to reduce computational power, after making sure that a higher taxa number would not substantially improve model statistics in climate reconstructions.
The number of taxa is limited by the modern training dataset from North America, which contains 70 taxa after applying our taxa harmonization routine (see details in  Herzschuh et al. , 2022b). We therefore restricted the number of taxa in all fossil datasets to keep the taxa comparable for the reconstructions. To identify the most common taxa, we used Hill's N2 diversity index (i. e. , the effective number of occurrences of a species in the dataset;  Hill, 1973). For all analyses, square root percentages were used, if not indicated otherwise. A modern pollen training dataset comprised of 15 379 sites includes datasets from Eurasia (EMPD1,  Davis et al. , 2013; EMPD2, Davis et al. , 2020; Herzschuh et al. , 2019; Tarasov et al. , 2011)  and North America  (Whitmore et al. , 2005). The modern pollen datasets were taxonomically harmonized in accordance with the fossil pollen dataset.
The site-specific T ann , T July , and P ann were derived from WorldClim 2 version 2. 1 (spatial resolution of 30 s; ∼ 1 km 2 ; https://www. worldclim. org, last access: 22 November 2021;  Fick and Hijmans, 2017) , by extracting the climate data at the location of the modern sample sites using the raster package in R  (version 3. 5-11, Hijmans et al. , 2021; R Core Team, 2020). The WorldClim 2 dataset provides spatially interpolated gridded climate data aggregated from weather stations as temporal averages between 1970-2000  (Fick and Hijmans, 2017). We used monthly average temperature data to extract the mean T July and the bioclimatic variables of bio1 (T ann ) and bio12 (P ann ).
Reconstruction methods Our reconstruction approach included MAT  (Overpeck et al. , 1985)  and  WA-PLS (ter Braak and Juggins, 1993) , by applying the MAT and WA-PLS functions from the rioja package (version 0. 9-21, Juggins, 2019) for R (R Core Team, 2020) on our northern hemispheric fossil pollen synthesis. For each fossil location, we calculated the geographic distance between each modern sampling site and the fossil pollen record using the rdist. earth function from the fields R package  (version 10. 3, Nychka et al. , 2020)  and selected a unique calibration set from modern sites within a 2000 km radius. We fixed the radius to 2000 km instead of 1500 km, as suggested in a study in eastern Asia by  Cao et al. (2017) , because the modern dataset density is rather low in northern Asia. For the reconstruction with MAT, we used the original pollen percentages of the selected fossil pollen taxa, looking for seven analogs between the pollen data and the selected calibration dataset.
To reconstruct T July , we identified the P ann range reconstructed by WA-PLS and extended it by 25 % to both ends of the modern P ann range in order to reduce the influence of P ann on the T ann and T July reconstruction due to covariation. We applied the same method to the reconstruction of P ann. T ann and T July were tailored by P ann ; P ann was tailored by T July and, additionally, by T ann (illustrated, for an example, in Appendix Fig. 2 ). Reconstruction uncertainties are provided as root mean square errors (RMSEs) derived from the output in the MAT and WA-PLS functions. Model errors in WA-PLS and MAT are reported as root mean square errors in the prediction (RMSEP) that are derived from the leave-one-out cross-validation. We provide site-or sample-specific measures of quality, in addition to the error estimates and model statistics, to allow the user to assess the quality of the climate reconstruction dataset.
First, we applied a canonical correlation analysis (CCA) to the modern training dataset in order to explore the modern relationship between the pollen spectra and the climate variables and to infer the explained variance in the modern pollen dataset by the target climate variables  (ter Braak, 1988 ) by using the cca function in the vegan R package (version 2. 5-7,  Oksanen et al. , 2020). The ratio between the constrained (λ 1 ) and unconstrained (λ 2 ) explained variance was determined for all modern training datasets used for climate reconstructions. High values of λ 1 vs. λ 2 ( 1) are commonly considered to be an indicator to measure how well the target environmental variable relates to the variation in the modern pollen dataset (e. g. ,  Juggins, 2013). However, most training datasets encompass multiple environmental variables that are often correlated, and additional requirements for such variables would be necessary to explain a significant and independent portion of the variation in the training dataset.