dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS The authors would like to thank  M. Misfeldt  for his insightful comment. The Albert Einstein Institute gratefully acknowledges the support of the  German Space Agency, DLR. The work is supported by the  Federal Ministry for Economic Affairs and Energy  based on a resolution of the  German Bundestag  (Grants No. FKZ 50OQ0501 , No. FKZ 50OQ1601 , and No. FKZ 50OQ1801 ). J. B. gratefully acknowledges support from the  UK Space Agency via STFC  ( ST/W002825/1 ). O. H. gratefully acknowledges support from the  Centre National d'Études Spatiales (CNES)  and the  Programme National GRAM of CNRS/INSU  with  INP  and  IN2P3  cofunded by  CNES.
Acknowledgments We would like to warmly thank  Professors Marion  and  Thomas Cremer , who kindly shared with us pictures of original drawings from  Carl Rabl  (Figure 1a),  Eduard Strasburger  (Figure 1b) and  Theodor Boveri  (Figure 1c), as well as  Professor Paul Fransz  for providing FISH pictures from his original article (Figure 1d ande). We also thank  Etienne Herzog  and  Gilles Dupouy  for useful and heated discussions on the topic. Financial support. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , by  ANR-18-CE20-0011  project  REWIRE  and  ANR-20-CE13-0025  project  MechaNUC.
Former shifts in these circulation mechanisms are essential to better constrain, as they may have played a critical role in pacing regional and possibly global Quaternary climate change. Here, we present a new set of cosmogenic 10 Be and 26 Al exposure ages from pre-Last Glacial Cycle moraine boulder, glaciofluvial outwash cobble and bedrock samples. This dataset constitutes the first direct chronology dating pre-LGM glacier advances in northern Patagonia, and completes our effort to date the entire preserved moraine record of the Río Corcovado valley system (43°S, 71°W). We find the outermost margins of the study site depict at least three distinct pre-Last Glacial Cycle glaciations occurring at 284 ± 7 ka, 257 ± 7 ka, and 147 ± 4 ka. Combined with the local LGM chronology, we discover that a minimum of four distinct Pleistocene glaciations occurred during Marine Isotope Stages eight, six, and two in northern Patagonia.
65 g cm -3 and elevation flag is STD. For 10 Be exposure-age calculations, we used the NIST_27900 standardisation (equivalent to 07KNSTD within rounding error) and the central Patagonia production rate  (Kaplan et al. , 2011)  obtained and re-calculated from the ICE-D online database (http://calibration. ice-d. org/). Details regarding specific AMS standards, nominal 10 Be/ 9 Be ratios and 10 Be half-life employed are included in the supplementary materials. 26 Al exposure ages were calculated using the Purdue Z92-0222 AMS standardisation (equivalent to KNSTD within rounding error) and the default CRONUS-Earth online calculator's global production rate of  Borchers et al. (2016) , as the central Patagonia production rate does not feature 26 Al/ 27 Al measurements. We however consider it reasonable to use both the central Patagonia and global production rates when comparing 10 Be and 26 Al ages, as their respective 10 Be calibrations agree within error, with reported LSDn scaling-fitting parameter values of 0.
823 ± 0. 067 and 0. 846 ± 0. 040, respectively (calibration: ICE-D online database). All exposure ages are displayed in Table  2. Below, we discuss exposure ages calculated using the time-dependent LSDn scaling scheme of  Lifton et al. (2014)  with 1σ analytical uncertainties. Exposure ages are between 6-9% and 0. 6-1% older when using the St and Lm scaling schemes of  Lal (1991)  and  Stone (2000) , respectively. The 10 Be New Zealand production rate  (Putnam et al. , 2010) , often used for Patagonian TCN exposure-age datasets due to its southern mid-latitude location (44°S), decreases our exposure ages by between 1. 7% and 1. 5% using LSDn scaling, which is less than 1σ analytical uncertainties (3. 5-1. 6%).
6 ka and afford a mean exposure age of 145. 3 ± 18. 4 ka. Younger ages from the remaining three boulders (RC20-03, 10, 11) plot outside the remaining boulder population's 95% confidence level and the 2σ envelope associated with the youngest RC II outwash cobble (RC20-07). We consider 10 Be inheritance an unlikely source of exposure-age scatter compared to the high potential for boulder exhumation causing young outliers. Moreover, the RC II outwash and RC II moraine belt are geomorphologically likely to represent the same glacier expansion event. Hence, these three younger boulder ages were rejected as stratigraphical and statistical outliers (Table  2 ). The three remaining moraine boulder ages still exhibit a high MSWD value of 28. 7 (>k = 3. 0), indicating that the remaining dataset displays substantially more exposure-age scatter than can be predicted solely by analytical uncertainties.
4 mm ka -1 ;  Kaplan et al. , 2005). To summarise, outwash cobble and bedrock ages suggest that outermost RC I glaciogenic deposits were likely deposited by a MIS 8 glacier advance (~270-245 ka), as portrayed by the oldest outwash cobble age (Figs. 6b, 7 ). On the other hand, the innermost and youngest outlet-glacier advance to have reached the multi-ridge, 4 km-wide RC I moraine complex (Fig. 1c , 6a) may have been significantly younger, and perhaps occurred during the MIS 7d interstadial. However, the latter part of this interpretation remains unclear, due to the lack of chronological data for the innermost RC I deposits combined with the analytical uncertainties and the large scatter displayed by outwash cobble exposure ages from this margin. More chronological constraints are thus required to test this hypothesis.
g. Putnam et al. , 2013; Kiernan et al. , 2014; Evans et al. , 2019; Fernandes et al. , 2021) , suggesting that the greatest global ice volume of the middle Pleistocene was likely reached during that interval  (Hughes et al. , 2020). In Patagonia, however, our chronology is amongst the first published datasets, along with one other ongoing investigation (abstract:  Peltier et al. , 2017) , offering direct and robust dating of a MIS 6 glacial expansion event. Indeed, the well-studied Lago Pueyrredón and Lago Buenos Aires pre- LGC moraine-outwash records do not provide clear chronological evidence for MIS 6 advances  (Hein et al. , 2009; 2017). However, this may just imply that the MIS 2 advances of the Lago Pueyrredón and Lago Buenos Aires outlet glaciers were more extensive than any MIS 6 glacial events.
(2021a, b) , our complete chronology reveals a minimum of four distinct expansion events of northeastern Patagonian Ice Sheet outlet glaciers that occurred during the MIS 8, MIS 6, and MIS 2 intervals. Further results gathered from bedrock surface and moraine boulder exposure ages also provide inceptive evidence for another MIS 7 expansion event and at least another MIS 6 advance previously reaching a close extent to that of the penultimate RC II advance.  Our dataset presents the first robust direct dating evidence of MIS 6 glaciations in Patagonia.  We did not find evidence for MIS 4 and 3 deposits in the RC valley geomorphological record. However, extensive glaciations did occur at these times on the former western margin of the Ice Sheet, and in other, more southern Patagonian regions.
Acknowledgements. We express our gratitude towards all individuals who contributed to the https://crowd. science/crowdfunding campaign that provided us with the means to conduct a field expedition to the study site in January and February 2020. We thank all local landowners who authorized access and sampling on their properties, in particular, the  Estancia Tecka (Chubut province, Argentina)  for enabling access to their land and private roads. We thank  Elaine McDougall  and  Allan Davidson  for their invaluable laboratory work enabling efficient quartz isolation and purification from our rock samples.
Financial support. This study was conducted within the context of a  University of Edinburgh E 3 Doctoral Training Partnership Ph. D. studentship  (award code:  NE/L002558/1 ) awarded by the  Natural Environment Research Council (NERC)  and the  University of Edinburgh School of GeoSciences  to  T. P. M. L. Fieldwork  ( 01/02 2020 ) was partly supported by a crowdfunding campaign through the Crowd. Science fundraising platform (https://crowd. science)and a  British Society for Geomorphology   Postgraduate Research Grant award  ( BSG-2019-04 ) awarded to T. P. M. L. TCN exposure dating laboratory analysis and AMS measurements were funded by a  NERC National Environmental Isotope Facility (NEIF)  grant ( 2245-0320 ) awarded to A. S. H. and T. P. M. L. in July 2020. Review statement. https://doi.
Data online repository. The data needed to re-calculate the 10 Be and 26 Al TCN exposure ages presented in this paper using the "online calculator formerly known as the CRONUS-Earth online calculators" version 3 is freely available online, as long as original publication is cited when used and/or referred to. This includes a textfile with data blocks in the correct input format and arranged by landforms studied in this investigation. The data are located in an open-access Mendeley Data online repository, accessible using this DOI: https://doi. org/10. 17632/gg4b3sh9k2. 1.
Abstract. Understanding the relationship between surface marine ecosystems and the export of carbon to depth by sinking organic particles is key to representing the effect of ecosystem dynamics and diversity, and their evolution under multiple stressors, on the carbon cycle and climate in models. Recent observational technologies have greatly increased the amount of data available, both for the abundance of diverse plankton groups and for the concentration and properties of particulate organic carbon in the ocean interior. Here we use synthetic model data to test the potential of using machine learning (ML) to reproduce concentrations of particulate organic carbon within the ocean interior based on surface ecosystem and environmental data. We test two machine learning methods that differ in their approaches to data-fitting, the random forest and XGBoost methods.
The synthetic data are sampled from the PlankTOM12 global biogeochemical model using the time and coordinates of existing observations. We test 27 different combinations of possible drivers to reconstruct small (POC S ) and large (POC L ) particulate organic carbon concentrations. We show that ML can successfully be used to reproduce modelled particulate organic carbon over most of the ocean based on ecosystem and modelled environmental drivers. XGBoost showed better results compared to random forest thanks to its gradient boosting trees' architecture. The inclusion of plankton func-tional types (PFTs) in driver sets improved the accuracy of the model reconstruction by 58 % on average for POC S and by 22 % for POC L. Results were less robust over the equatorial Pacific and some parts of the high latitudes.
Introduction Progress in numerical ocean modelling over multiple decades coupled with fundamental knowledge of fluid dynamics have led to an explicit representation of ocean dynamics in Earth system models and of most of its key features, apart from small-scale features which are parameterized. In contrast, ecosystem dynamics in ocean biogeochemical models are Published by Copernicus Publications on behalf of the European Geosciences Union. A. Denvil-Sommer et al. :  Testing the reconstruction of modelled particulate organic carbon much more reliant on empirical data for growth and loss processes, with the theoretical basis limited to the dynamic representation of interactions among lower trophic levels (zooplankton and smaller organisms) and their influence on carbon pools and fluxes (Le  Quéré et al.
Large PFTs contribute mostly to POC L , while small PFTs contribute mostly to POC S  (Le Quéré et al. , 2016; Fig. 2). The NEMO-PlankTOM12 model output was sampled at the time and location identified from the observations mentioned above to create a synthetic data set. The model gridcoordinate closest to the real geographical position was chosen. If several measurements were co-localized at the same grid coordinate and same time step (day for daily Plank-TOM12 and month for monthly PlankTOM12 outputs), it is counted as one measurement. This model sampling produced  400 positions when using the daily or monthly PlankTOM12 outputs. All drivers and targets were taken from the model output at the corresponding coordinates up to 1400 m depth. These outputs served as the reference for validation and evaluation of the ML methods and for establishing the sets of the most important drivers.
Method We tested two ML methods that are widely used in targets' reconstruction based on tabular data sets: the ran-dom forest regressor and the XGBoost (Extreme Gradient Boosting) regressor. The random forest (RF) regressor is an ensemble algorithm that contains a number of decision trees on various subsets of the given data set and takes as output the average of prediction from each tree estimator. RF can run several trees at the same time, allowing the use of a large number of input variables, and it is robust to overfitting  (Biau, 2012). The XGBoost (XGB) regressor is an effective tree-based ensemble learning algorithm  (Chen and Guestrin, 2016). It builds several models sequentially, where each new model attempts to correct errors from the previous one. XGBoost uses the gradient descent algorithm to minimize the loss function of the model.
Using RF and XGBoost, we can estimate the driver importance to identify which driver has the greatest impact on the predictions. To check the driver importance, we use the drop_col_feat_imp Python function (https://gist. github. com/ erykml/6854134220276b1a50862aa486a44192, last access: 18 May 2023). This method estimates how the accuracy of the ML output changes if one of the drivers is dropped off  between validation and test data can demonstrate an effect of data mismatch. It is worth noting that RF does not necessarily need a validation data set as they perform internal validation. During the training algorithm, each tree is constructed from a random subset of original data. Usually it represents two-thirds of data, and one-third of data is used to estimate out-of-bag error to assess model performance.
XGB uses a validation data set to evaluate the model during training and to prevent overfitting by applying an early stopping. In the present study, the available data were split into training and validation data sets (Fig. 3a ). Validation data are not included in RF training; however we use them to test the performance of trained RF and tune hyperparameters afterwards. The test data are taken from the regions where there are no observations (Fig. 3b ), i. e. 3 months for each year from the period 2009-2013, and six positions for each month were chosen randomly. This will allow us to identify the possible accuracy of reconstruction that can be reached in these regions when we apply a developed method to real observations. However, when POC S and POC L are reconstructed using only realworld observations, we will need to split all available data into training, validation and test data sets.
We use the RandomForestRegressor function from scikitlearn (https://scikit-learn. org/stable/modules/generated/ sklearn. ensemble. RandomForestRegressor. html, last access: 18 May 2023) with its default parameters and min_sample_leaf equal to 20. To apply the XGBoost regressor, we use XGBRegressor from xgboost (https:// xgboost. readthedocs. io/en/stable/python/python_intro. html, last access: 18 May 2023). Parameters were set as follows: n_estimators = 2000, max_depth = 7, eta = 0. 01, subsample = 0. 7, colsample_bytree = 0. 8, gamma = 0. 01 for POC L and gamma = 0. 3 for POC S , and early_stopping_rounds = 10. We tested 27 driver sets (DSs) that are summarized in Table 1. For each DS, we identify the most important drivers that influenced the reconstruction of small (POC S ) and large (POC L ) particulate organic carbon concentration.
Development of the machine learning method We tested 27 sets of drivers (Table  1 ) and two ML methods, random forest (RF) and XGBoost regression (XGB). Figure  5  shows the statistics of POC S reconstruction using RF and XGB. XGB (orange) generally outperforms RF (blue). The statistics are slightly worse for the validation and test data sets, as expected. For reconstructions using XGB, the RMSE and absolute bias are about 0. 05 and 0. 03 µmol L -1 on the training data set and vary around 0. 1 and 0. 05 µmol L -1 , on the validation and test data, respectively. Correlation coefficients (Fig. 5g, h, i ) have high values for all data sets, showing that the vertical profiles of POC S have the correct shape. These results show that the available Table  2. Correlation coefficient between small (POC S ) and large (POC L ) particulate organic carbon concentration and possible drivers.
Driver Figure  6  shows the statistics of POC L reconstruction using RF and XGB. XGBoost again slightly outperforms RF for most driver sets. Results for driver sets with PFTs show lower RMSEs and absolute biases and higher correlation coefficients. Except for the effect of PFTs on the POC L reconstruction, we did not observe a clear influence of one driver or group of drivers. Using XGBoost, the reconstruction of POC L shows the RMSE in DS1 is high at 0. 03 µmol L -1 , while it is in the range of 0. 021-0. 026 µmol L -1 in DS3-DS27, with absolute bias in DS1 of 0. 02 and 0. 015-0. 018 µmol L -1 for DS3-DS27 based on test data (Fig. 6c, f ). Likewise, there is a correlation coefficient of 0. 56 for DS1 and between 0. 7 and 0. 77 for DS3-DS27 based on the training data set (Fig. 6g ). We estimated the ranking of importance for each driver averaged over 27 driver sets (Table  1 ) for RF and XGB (Fig.
XGBoost regressor builds the model sequentially, improving it at each iterative step. At each iteration, XGBoost regressor analyses the prediction and gives more weight to the data where the fit is still wrong. It is a good tool for an unbalanced data set, like in our case where the data of particulate organic carbon concentration are sparse in time and space. We tested the influence of a wide range of environmental and ecosystem drivers on POC S and POC L reconstruction. The introduction of plankton functional types (PFTs) in the driver set greatly improves the fit and shows a linkage between surface ecosystem structure and particulate organic carbon distribution within the ocean interior. We improved the accuracy of POC S reconstruction by 59 % on RMSE and 63 % on absolute bias and by 52 % on correlation by introducing PFTs in the driver sets (from the comparison of DS1 and DS25).
Therefore the ML reconstructions assign open-ocean processes to coastal regions, leading to significant biases. The tropical eastern Pacific is a region of strong interannual variability, and the sparse measurements in time make it harder to capture this variability correctly. Other regions with poor coverage by observations -the eastern Indian Ocean, the western Pacific Ocean and the Southern Ocean -show the statistics of reconstruction comparable to one from regions with a good cover -regions in the Atlantic Ocean. However, we found that the Southern Ocean is a more sensible region to the driver set's composition. The observational data are particularly sparse in this region, and our analysis suggests that identifying the drivers of importance based on real data set will be difficult. Here we showed that the XGBoost regressor and random forest are suitable for this problem and can reconstruct modelled POC S and POC L with appropriate accuracy.
Acknowledgements. The authors would like to thank  Jean-Olivier Irisson  for his contribution in the development of the methodology. Anna Denvil-Sommer ,  Erik T. Buitenhuis  and  Corinne Le Quéré  acknowledge support from the  Royal Society  (grant  RP\R1\191063 ) and the  NERC Marine Frontiers  project (grant  NE/V011103/1 ) for  Corinne Le Quéré. Rainer Kiko  acknowledges support via a " Make Our Planet Great Again " grant from the  French National Research Agency  within the " Programme d'Investissements d'Avenir"  (grant no. ANR-19-MPGA-0012 ) and by the  Heisenberg programme   of the German Science Foundation  under project number  469175784. Financial support. This research has been supported by the  Royal Society  (grant no. RP\R1\191063 ). Review statement. This paper was edited by  Xiaomeng Huang  and reviewed by two anonymous referees.
Code and data availability. PlankTOM12 data used within this study are available at https://doi. org/10. 5281/zenodo. 7324781 (Denvil-Sommer, 2022a). UVP5 data can be found at https://doi. org/10. 1594/PANGAEA. 924375  (Kiko et al. , 2021). Codes for data preparation, development of machine learning methods and tests of different driver sets, as well as codes that provide figures shown in the article, can be found at https://doi. org/10. 5281/zenodo. 7326992 (Denvil-Sommer, 2022b).
WHAT THIS STUDY ADDS ⇒ This systematic review found inadequacies and inconsistencies in the quality of reporting of adverse events of drugs used for the treatment of covid-19 in published articles ⇒ One in three studies had a low or very low score for quality of reporting according to the CONSORT (Consolidated Standards of Reporting Trials) statement ⇒ Compared with serious adverse events reported in trial summaries on ClinicalTrials. gov, about 51% of serious events were not reported in published clinical trials.
HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE, OR POLICY ⇒ Use of the new CONSORT harms extension might improve reporting of safety data ⇒ Some publications under-reported adverse events and therefore researchers and clinicians should consult several sources of information to correctly establish the safety profile of drugs ⇒ The pharmacovigilance of these drugs is affected because assessment of the benefit-risk ratio of these covid-19 drugs based on these clinical trials is not precise ABSTRACT OBJECTIVE To assess the quality of reporting of adverse events in clinical trials of covid-19 drugs based on the CONSORT (Consolidated Standards of Reporting Trials) harms extension and according to clinical trial design, and to examine reporting of serious adverse events in drug trials published on PubMed versus clinical trial summaries on ClinicalTrials.
gov. DESIGN Systematic review. DATA SOURCES PubMed and ClinicalTrials. gov registries were searched from 1 December 2019 to 17 February 2022. ELIGIBILITY CRITERIA FOR SELECTING STUDIES Randomised clinical trials evaluating the efficacy and safety of drugs used to treat covid-19 disease in participants of all ages with suspected, probable, or confirmed SARS-CoV-2 infection were included. Clinical trials were screened on title, abstract, and text by two authors independently. Only articles published in French and English were selected. The Cochrane risk of bias tool for randomised trials (RoB 2) was used to assess risk of bias. RESULTS The search strategy identified 1962 randomised clinical trials assessing the efficacy and safety of drugs used to treat covid-19, published in the PubMed database; 1906 articles were excluded after screening and 56 clinical trials were included in the review.
Among the 56 clinical trials, no study had a high score for quality of reporting of adverse events, 60. 7% had a moderate score, 33. 9% had a low score, and 5. 4% had a very low score. All clinical trials with a very low score for quality of reporting of adverse events were randomised open label trials. For reporting of serious adverse events, journal articles published on PubMed under-reported 51% of serious adverse events compared with clinical trial summaries published on ClinicalTrials. gov. CONCLUSIONS In one in three published clinical trials on covid-19 drugs, the quality of reporting of adverse events was low or very low. Differences were found in the number of serious adverse events reported in journal articles versus clinical trial summaries. During the covid-19 pandemic, risk assessment of drugs in clinical trials of covid-19 drugs did not comply with good practice recommendations for publication of results.