dataesr/scientific-paragraphs-categorization huggingface dataset:
For example,  Fu (2016)  found SL thermo trend at 0. 88 mmyr -1 (2003-2014) that is similar to the estimate presented here, but derived a trend in OHC of 0. 66 Wm -2 using ε = 0. 13 mYJ -1 after  Wunsch and Heimbach (2014). Assuming ε = 0. 12 ± 0. 03 mYJ -1 yields a change in OHC at 0. 52 Wm -2 per 1 mmyr -1 change in sea level, which is consistent with  Trenberth and Fasullo (2016). Based on this value  Dieng et al. (2017)  derived an OHC trend from alti-GRACE over 2003-2013 at 0. 65 ± 0. 1 Wm -2 (uncertainty represents trend uncertainty only). To avoid systematic errors and to decrease the uncertainty in OHC requires more detailed assessment of ε and ideally the production of ε datasets for the SL thermo conversion to OHC. These datasets potentially can be derived from observational datasets or ocean reanalysis/models that capture the entire ocean column globally.
COMPARISON OF GLOBAL MEAN SEA LEVEL BUDGET, OCEAN HEAT CONTENT AND EEI ESTIMATION In this section, we compare the OHC estimates obtained from the different approaches described above, namely the remote sensing technique (altimetry and GRACE), the analysis of in situ observations of temperature as well as ocean reanalysis. We furthermore compare time series of Ocean Heat Storage (OHS), the time derivative of OHC, to EEI as measured at TOA from CERES (EBAF,  Loeb et al. , 2017)  dataset. The CERES data is known to be temporally stable (<0. 17 Wm -2 at interannual time scales,  Loeb et al. , 2018a) , but biased in the long-term global mean (and therefore adjusted to agree with long-term OHC trends based on Argo measurements,  Loeb et al. , 2009). The goal of this comparison is to gain insight into the capabilities and weaknesses of the individual approaches, and to formulate recommendations that lead to improvements in retrieval and the complementary use of the datasets.
g. ,  L'Ecuyer et al. , 2015). From this comparison it is obvious that a reliable estimate for long-term global mean EEI from surface heat flux is not possible, as there is both substantial overestimation and random error. The limitations of current data products of surface heat flux are outlined in section "Estimating the Ocean Surface Net Flux From Space Observations. " The character of these datasets is complementary to the other approaches, in that they provide constraints at the ocean surface, allow for studies of the separate contributions of ocean and atmospheric energy variability  (Liu et al. , 2017) , and allow regional analysis that can provide information on the magnitude and direction of lateral heat fluxes (e. g. ,  L'Ecuyer et al. , 2015). The emerging ITOT method (see Estimating the Ocean Temperature From in situ Observations) is also absent from our comparison because it is an emerging method that is not operational yet and needs further improvements.
The alti-GRACE sea level budget approach provides a full depth estimate of OHC but is associated with uncertainties that are slightly larger than what is needed to pin down the global mean value of EEI within needed accuracy levels (e. g. , Table  3  and von  Schuckmann et al. , 2016; Hakuba et al. , 2018). The alti-Grace approach is promising as it provides consistent spatial and temporal sampling, is representative of the entire global oceans, and estimates OHC over the ocean's entire depth. A number of issues need to be addressed to improve such datasets, comprising improved estimates of heat expansion efficiencies needed to translate thermosteric sea level change to OHC changes (which relies on in situ measurements of temperature and salinity), analysis of footprint discrepancies and their impact, the retrieval of thermosteric sea level at regional scale including the varying role of halosteric effects, as well as a range of technical challenges in the retrieval of both SL mass and SL total , such as inter-calibration, orbit and geocenter corrections, and improved GIA and background models (see Estimating the Ocean Thermal Expansion From Space Observations).
We also calculated the area of native vegetation at risk of loss per 1000 metric tons of soybeans produced in each region of the Cerrado from 2021 to 2050. This allowed us to estimate which traders are expected to contribute most to future native vegetation conversion in the Cerrado based on our model projections and the TRASE (Transparent Supply Chains For Sustainable Economies) dataset of market share by region  (1). We report the aggregate results for the six largest soy traders [ADM (Archer Daniels Midland), Amaggi, Bunge, Cargill, COFCO, and Louis Dreyfus Company-hereafter referred to as the Big 6], the ABIOVE (the Brazilian Association of Vegetable Oils Industry) and ANEC (Brazil's National Association of Grain Exporters) traders associations (which includes the Big 6), and the Chinese and the 28 European Union (EU) export markets, as well as Brazil's domestic consumption, assuming stable market shares of each group by 2050.
Initiatives such as the Consumer Goods Forum, the Tropical Forest Alliance 2020, and the 2014 New York Declaration on Forests are laudable and cover multiple commodities and regions by various stakeholders, including public, private, and civil society actors, but may fail to provide a clear pathway for the implementation of their targets  (18). Zero-deforestation agreements such as the SoyM are considered easy to monitor and effective in reducing deforestation for a specific commodity, although leakage must be addressed to meet deforestation goals  (18). The Cerrado Manifesto indicates an increasing recognition of the vanishing Cerrado vegetation and asks the actors in commodity supply chains to protect this biome. Our risk analysis, based on our baseline projections and on the TRASE dataset, reveals which traders and markets have the highest future exposure to soy-related native vegetation loss.
The volume of unknown trade flows in the TRASE dataset (  1 ) is approximately 10% and is not associated with any trader. Furthermore, the use of the 2015 TRASE dataset implies that the market shares of individual companies remain constant over time, which oversimplifies the dynamics of the soy market. To evaluate the sensitivity of our results to the main drivers of land use change in Brazil, we also run our main scenario for two contrasting shared socioeconomic pathways (SSPs), SSP1 "sustainability" and SSP3 "fragmentation," which projects different demands for soy. In comparison to their respective baselines, the SoyM scenario would avoid 5. 2 Mha (SSP1) and 3. 4 Mha (SSP3) of direct native vegetation conversion to soybeans in the Cerrado biome. Taking into account the leakages to other land uses, the SoyM still prevents the loss of 2.
The SSP1 and Table  2. Future native vegetation conversion risk. The columns show per trader, trader associations, or consumer market; the future soy sourced in million metric tons (Mt); the future native vegetation conversion risk or the area at risk of being converted to soy in million hectares (Mha); and the relative future native vegetation conversion risk in hectares per 1000 metric tons of soy sourced by a given trader, trader association, or consumer region over 30 years. The projections on soy area and production are from the baseline scenario projections of GLOBIOM Brazil between 2021 and 2050, and the market share of companies or consumer regions are from the TRASE dataset for the year 2015  (1). (25). Productivity can increase endogenously in the model through shifts between management systems (from low to high input) or from the reallocation of production to more suitable areas, which allows endogenous changes of yields in response to market signals.
Native vegetation conversion risk We estimated the absolute future native vegetation conversion risk (measured in hectares) per trader or consumer market in each grid cell of the Cerrado biome as the product between the total area directly converted from native vegetation to soy between 2021 and 2050, as projected by the baseline scenario and the market share of traders, traders associations, or consumer regions tracked by the TRASE dataset (1) for the year 2015 (see Table  2 ). We also computed the relative future native vegetation conversion risk for a trader, trader association, or consumer market by dividing its future risk by its amount of soy traded during a given period. This indicator is a measure of the native vegetation loss intensity of each trader, trader association, or consumer market regarding its volume of soy sourced in 30 years (from 2021 to 2050), i.
Acknowledgments Funding: This study was supported with grant funding from the  Science for Nature and People , a partnership of  The Nature Conservancy, Wildlife Conservation Society , and the  National Center for Ecological Analysis and Synthesis at the University of California, Santa Barbara. This work was also supported by a grant from the  Gordon and Betty Moore Foundation  (no. 4641 ), by the  FeslerLampert Endowment at the University of Minnesota , by the  National Wildlife Federation as part of the Collaboration for Forest and Agriculture , and by the  RESTORE+  project, which is part of the  International Climate Initiative (IKI) , supported by the  Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU)  based on a decision adopted by the  German Bundestag.
Author contributions: A. C. S. , F. M. R. , J. F. , and S. P. conceived the idea, designed the study, and led the writing and revision of the manuscript. A. M. , P. R. A. , L. B. , J. P. , M. O. , F. K. , G. C. , and A. X. Y. C. helped to design the study. A. C. S. and F. M. R. adapted and run the model for this study. P. R. A. and J. P. led the preparation of the database for risk analysis. All authors discussed the results, revised the manuscript, and contributed to its improvement. Competing interests: The authors declare that they have no competing interests. Data and materials availability: All data needed to evaluate the conclusions in the paper are presented in the paper and/or the Supplementary Materials. Additional data related to his paper may be requested from the authors. Submitted 4 November 2018 Accepted 13 June 2019 Published 17 July 2019 10.
Introduction Unexpected Food-Drug Interactions (FDIs) occasionally result in treatment failure, toxicity and an increased risk of side-effects. While drug-drug interactions can be investigated systematically, there is a much larger number of possible FDIs. Therefore, these interactions are generally discovered and reported only after a drug is administered on a wide scale during post-marketing surveillance. A notable example is the discovery that grapefruit contains bioactive furocoumarins and flavonoids that activate or deactivate many drugs in ways that can be life-threatening  (Dahan and Altman, 2004). This effect was first noticed accidentally during a test for drug interactions with alcohol that used grapefruit juice to hide the taste of ethanol. Currently, information about FDIs is available to medical practitioners from online databases such as DrugBank 1  and compendia such as the Stockley's Drug Interactions  (Baxter and Preston, 2010) , but these resources have to be regularly updated to keep up with a growing body of evidence from biomedical articles.
The approach takes as input a sample set of relevant documents that are cited in the Stockley compendium. In this way, the problem of FDI discovery from biomedical literature is limited to the task of interaction candidates search, that is the task of finding documents that describe FDIs from a large bibliographic database. We make use of a large corpus of relevant publications to investigate index terms used to annotate articles about FDIs and we propose an automated method for query selection that increases recall. The main contributions of this work are: • a discriminative model for automatically constructing high-coverage and domain-specific corpora for information extraction, • an approach for automatically selecting queries using index terms as candidates, • an automated method to evaluate queries based on a sample corpus.
The paper is structured as follows. We begin by discussing several design decisions for the subtask of classifying documents based on relevance, adopting a discriminative model for information retrieval in Section 3. In Section 4, we introduce the subtask of query selection discussing candidate term selection and several methods for scoring queries. Section 5 describes the datasets used Figure  2 : Workflow for automated corpus construction using a collection of sample documents and a search engine to evaluate our approach for automatically constructing a corpus for FDIs and Section 6 presents the results of an empirical evaluation. Then we provide an overview of related work for this task in Section 7 and we discuss a formal definition for the problem at hand in Section 2. We conclude this work in Section 8.
In our task, features are constructed from words contained in all documents. Feature representation. To train classification models, the dataset is transformed into a matrix of size N × M where N is the number of documents in the dataset and M is the number of features. For each word feature, three types of feature representation approaches are investigated for representing input data: • One-hot encoding. Raw binary occurrence (RBO) matrices. Each document d is represented as a binary feature-document occur-rence vector Rbo = [rbo 0 , rbo 1 ,. rbo m ] of size M where rbo i = 1 if the feature i is in the document d, 0 otherwise. • Term frequency. Count occurrence matrices. Each document d is represented by a vector of counts of term-document occurrences T f = [tf 0 , tf 1 ,. tf m ] of size M where tf i is the number of occurrences of the feature i in the document d.
Document classification evaluation For the purpose of selecting relevant documents regarding food-drug interactions, we evaluate several configurations to construct an efficient classification model. Three sets of experiments are designed around the three training datasets described in the previous section. For each case, we evaluate the models using average of Precision (P), Recall (R) and F1-score (F1) using 10-fold crossvalidation. Figure  3  shows the cross-validation re-sults for different word-based features described in Section 3. The best results in terms of F1score are obtained across all datasets for TF-IDF features with an SVM classifier. TF-IDF of unigram features combined with SVM classifier produce the best F1-score on all datasets. Focusing on these configurations, results are detailed in Table 4 where we can notice that the recall is higher for the third dataset.
The best F1-score presents a low standard deviation, which shows that the obtained model is relatively stable. We conclude that results are better on datasets that use a more restrictive filter for selecting the negative examples (Experiment 3). This demonstrates that the random sampling approach for the majority class can benefit from using a more informed strategy than selecting documents from the full collection. The next set of experiments is focused on evaluating the performance of features based on index terms as can be seen in Table  5. All the index terms that are used to annotate at least 10 documents from our collection are considered as features, ignoring the less frequent index terms. In general, the results are comparable or better than the best results using word features in terms of F1score.
Query selection evaluation The challenge for evaluating queries is that it is preferable to rely on the training examples alone for evaluation. But each selected query will retrieve documents that might be relevant but that are not contained in the provided dataset. To address this issue, we use the best performing classification approach described in the previous section to predict the relevance of retrieved documents instead of computing precision based on the docu- Word-based query candidates are not further considered at this stage because the best classification performance is achieved for 1-gram features which are deemed to be too ambiguous for our purposes. Table  6  gives an overview of the top 30 1-gram features selected using the SVM classifier. Several names of drugs such as aminophylline, cyclosporine, and ephedrine that are known to have interactions with foods are among the highest ranked features.
Foods such as caffeine, coffee, cola and grapefruit are also known for their high potential of interactions with drugs. Among these features, names of plants with drug interactions are present including biloba and kava. Although interesting on their own, we conclude that these features are too generic to be used as queries to extract articles about FDIs without further combining them with other features or index terms. On the other hand, index term candidates are much more precise, including many terms that refer to food-drug interaction mechanisms such In contrast, we propose an automated approach for query selection and we make use of a considerably larger dataset of relevant publications for training and evaluation. The food-drug interaction discovery task proposed here is similar in setting with the subtask on prior art candidates search from the intellectual property domain  (Piroi et al.
, 2011). In the CLEF-IP datasets, topics are constructed using a patent application and the task is to identify previously published patents that potentially invalidate this application. Keyphrase extraction approaches were successfully applied to generate queries from patent applications  (Lopez and Romary, 2010; Verma and Varma, 2011). The input is much larger for our task, that is a corpus of scientific articles describing FDIs manually annotated with index terms from the MeSH thesaurus. A main difference between our work and the CLEF-IP task is that we mainly focus on evaluating different methods for query selection by relying on the PubMed search engine. This makes our task more similar to the term extraction task  (Aubin and Hamon, 2006) , as we aim to identify relevant terms for a broad domain rather than for a specific document, as done in keyphrase extraction.
The dataset used in  (Jovanovik et al. , 2015)  to infer interactions between drugs and world cuisine is based on textual information from DrugBank about food-drug interactions and optimum drug intake time with respect to food. But this information was manually extracted and structured. The most closely related work to ours is  (Bordea et al. , 2018)  where the authors propose an approach for query selection based on index terms. We extend this work by considering multiple types of classification algorithms and by analysing different query candidates beyond index terms.
Conclusion and future work In this paper, we introduced a large dataset of articles that describe food-drug interactions annotated with index terms to investigate an approach for query selection that allows us to discover other food-drug interactions using an existing search engine. We investigated different strategies for addressing the problem of unbalanced data and we showed that a more informed approach that takes into consideration publication venue and year gives better results than a naive approach for random sampling. We proposed an automatic evaluation of retrieved results using a highperformance classifier and we showed that feature selection approaches outperform frequency-based approaches for this task, with an approach based on gain ratio achieving the best results in terms of predicted F1-score.
In our experiments mainly focused on queries constructed using a single index term, therefore a first direction for future work is to investigate more complex queries that combine multiple terms. The number of queries that have to be evaluated would increase considerably especially for combinations with word-based features. Another improvement would be to compare our results with keyphrase extraction approaches instead of analysing all the n-grams and to generate queries using background knowledge about drugs and foods. Finally, the datasets proposed here are based on an older version of the Stockley compendium from 2008. The results presented in this work could be more relevant if a more recent version is considered as this is a highly dynamic field of research.
Two additional heuris- tics rank higher retrieved articles that are anno-tated with the MeSH terms Drug Interactions and Plant Extracts/pharmacology. Another limitation of this work is the size of the evaluation dataset that is based on a single review paper (Izzo and Ernst, 2009) that provides about 100 references. as Biological Availability and Cytochrome P-450 Predicted Recall Predicted CYP3A. Scoring function P@100 @16k F1-score Frequency 0. 2020 0. 0032 0. 0584 TF-IDF 0. 2590 0. 0084 0. 0784 Info gain 0. 2755 0. 0084 0. 0812 Gain ratio 0. 3755 0. 0557 0. 0970 Correlation 0. 2590 0. 0081 0. 0770 Table 7: Scoring functions evaluated for the top 20 MeSH terms using predicted precision at top 100, re- call at top 16k and the combined predicted F1-score 7 Related work Hand-crafted queries based on MeSH terms are often used for retrieving documents related to ad- verse drug effects (Gurulingappa et al.
After several reversible oxygen exchange cycles, the oxygen composition of the sample cooled down to room temperature was considered stable and the δ value at room temperature was determined using a complete reduction of the sample under Ar/5% H2 flow  [22]. Cationic valences in Pr2NiO4+δ were investigated using XANES (X-ray Absorption Near-Edge Spectroscopy) at the nickel K and praseodymium LIII absorption edges (8333 and 5944 eV, respectively). Spectra were recorded using synchrotron radiation on the BM23 beam line at the European Synchrotron Radiation Facility (ESRF), France  [23]. Because the two considered absorption edges are quite closed the data were collected using the same beam line settings. The measurements were carried out in the transmission mode, using a Si (111) fixed exit double crystal monochromator.
Harmonic rejection was obtained by the use of a silicon coated mirror after the monochromator. Additional data were collected at room temperature using appropriate standard materials (i. e. with single and well known cationic oxidation states) such as NiO, LaNiO3, PrF3 and Pr0. 5Ce0. 5O2 to provide references for the Ni 2+ , Ni 3+ , Pr 3+ and Pr 4+ absorption edges, respectively. PrF3 was kept in a glove box, before transportation in an airtight container. The XANES spectra of Pr-based oxides exhibit one main line assigned to 4f 2 (Pr 3+ ) and, in addition in the case of Pr 4+ compounds, a higher energy satellite due to the 4f 1 final state configuration. However, in mixed valence Pr-based compounds (Pr 3 + Pr 4+ oxides) one should notice that the replicate splitting, due to the two possible configurations, is about 8-9 eV when the passive electrons are included.
In order to investigate the redox processes in Pr2NiO4+δ, XANES data were collected at temperatures ranging from 25°C to 700°C. This was achieved using an internal heating element with temperature measurement managed by an external control unit. In-situ data were collected isothermally on heating cycle using a 10°C/min rate, under controlled atmosphere with 20 vol% oxygen and 80 vol% helium gas flowing. Absorbance data were background subtracted and normalized. The absence of energy shift during the measurement was confirmed through the use of Ni reference, collected simultaneously to the sample of interest. The nickel K-edge position was found by taking the position of the zero-crossing in the second derivative of the corresponding XANES spectrum. The intrinsic error on the determination of the zero-crossing point has been defined as 0.