dataesr/scientific-paragraphs-categorization huggingface dataset:
Biogenic emissions are estimated with the MEGAN online model  (Guenther et al. , 2006) , mineral dust with the scheme from  Alfaro and Gomes (2001)  and  Menut et al. (2005b) , and sea salt with that from  Monahan (1986). In the present study, we use a setup of CHIMERE that offers a fair match with respect to observational datasets. These simulations are run with horizontal resolution of 20 km × 20 km and 15 vertical levels from 998 to 300 hPa. Table  1  presents a brief description of the main elements of the model. CHIMERE uses anthropogenic emissions from the HTAP v2. 2 inventory for 2010, on a monthly basis (https://edgar. jrc. ec. europa. eu/htap_v2/ last access: 14 September 2021,  Janssens-Maenhout et al. , 2015). The original inventory is considered for STD simulations in 2019 and 2020, whereas for the COVID emissions in 2020 a relative reduction for each activity sector (road transport, residential, industry, aviation, and shipping) is applied.
Results For better understanding the information provided by observations and simulations, the current multi-data analysis is presented in several steps. First, we focus on the total changes of ozone pollution between 2020 and 2019 (Sect. 3. 1) that are directly observed by in situ sensors and from space, and which we compare with the corresponding amount simulated by the model. Then, we analyse the modelderived changes between these 2 years but only associated with meteorological conditions (Sect. 3. 2). Finally, we compare changes of ozone pollution only linked with the pandemic lockdown conditions, estimated from models and observations (Sect. 3. 3). The originality of these results resides in the use of in situ and satellite observational estimates of the changes in ozone pollution associated the lockdown conditions O 3 covid obs&mod , which are adjusted for subtracting meteorology effects.
Satellite and in situ surface measurements The first step of our study consists of analysing all available datasets in terms of the changes in ozone pollution over Europe between the pandemic lockdown period in 2020 (focused here on 1-15 April) with respect to the same period during the previous year (this difference is hereafter called O 2020-2019   3   ). Figure  1  shows a comparison between the two observational datasets, while Fig. 1b  only considers in situ data coincident in time and space with satellite data (although very few differences are seen for the average of the whole in situ dataset). A good agreement is shown in terms of regional ozone patterns between IASI+GOME2 satellite data and in situ surface measurements. Both datasets clearly show similar structures of positive and negative anomalies.
Ozone enhancements in 2020 with respect to 2019 are seen both by satellite and surface data over northern and eastern France, western and southern Germany, northern Italy, and southwest England. Ozone photochemistry in these regions is typically dominated by VOC-limited conditions, observed both in standard situations (e. g. Beekmann and Vautard, 2010, Wilson et al. , 2012)  and during the pandemic lockdown (e. g. Menut et al. , 2020; Gaubert et al. , 2021). Both measurements also show clear ozone reductions in 2020 for less urbanized regions typically characterized by NO x -limited chemical conditions: Spain, southern Italy, Poland, and western England. A near-neutral or intermediate behaviour is seen over eastern Germany (in consistency with Beekmann and Vautard, 2010), with rather limited reductions of ozone both for IASI+GOME2 and surface data.
In quantitative terms, a scatterplot of co-located IASI+GOME2 and in situ data in time and space is presented in Fig. 2a. Colours represent the number of occurrences for intervals of 2 ppb of ozone in both axes. The correlation coefficient between these datasets is 0. 55, while the standard deviation of both datasets is practically the same and the root-mean-squared (rms) difference is 11. 8 ppb. When considering the average of the whole in situ dataset, the only notable change is an increase in the standard deviation of 13 % for the surface data (see Table  2 ). In all cases comparing daily averages of in situ data, there is an average difference or shift of ∼ 8. 6 ppb for the average    Cuesta et al. , 2013). Therefore, the negative difference for IASI+GOME2 with respect to surface concentrations may suggest that the reduction of ozone concentrations in 2020 with respect to 2019 at atmospheric layers roughly ∼ 2 km a.
However, the model depicts limited changes over Poland and enhancements over eastern Europe and the western Mediterranean, whereas both observations (satellite and when available in situ) depict clear reductions in 2020 with respect to 2019. The scatterplot of Fig. 2b  shows a fair correlation (0. 58) of CHIMERE with respect to in situ data. However, the model clearly underestimates the variability of the ozone changes in spring 2020 with respect to the previous year, as compared to in situ measurements (larger by more than a factor ∼ 2). Whereas O 2020-2019   3 values observed at the surface range from roughly -12 to +12 ppb in terms of daily averages, simulated values only spread from -6 to +6 ppb. The range of values of IASI+GOME2 has the same amplitude as the in situ data but shifted towards smaller values (-20 ppb to +5 ppb).
Generally, simulations show total anomalies in 2020 (with respect to 2019) that are only partially associated with the regions of typical NO x -limited and VOC-limited chemical regimes. On the other hand, observations from both in situ sensors and satellite do show clearer similarities between the typical regimes (in the regions where both datasets are available) and ozone anomalies during the pandemic lockdown. This might be linked to a large influence of meteorological conditions in the model, as compared to lockdown-induced emission changes. Still another reason for these differences could be that boundary conditions outside the simulation domain of CHIMERE do not account for the changes in an- thropogenic emissions linked with the pandemic in 2020 at a large scale. This would lead to a positive bias in simulated differences if part of the observed decrease at the free troposphere (by  Steinbrecht et al.
The satellite-derived estimate of lowermost tropospheric ozone changes during 1-15 April 2020 (Fig. 7a ) is clearly consistent with that derived from surface in situ measurements (Fig. 7b ), both adjusted for avoiding the effects of meteorological conditions. The location of the positive and negative anomalies of ozone satellite retrievals is like that of total changes between 2020 and 2019 (Fig. 1a ). Although, as for surface concentrations, the satellite-derived amplitude of the ozone enhancements linked to the lockdown over central Europe is clearly less pronounced than the total changes. Clear lockdown-derived enhancements of LMT ozone retrievals are seen over the Rhone Valley (reaching the Mediterranean), northern and eastern France, the Po Valley, and eastern England. Satellite data offer an extended geographical coverage with respect to surface in situ measurements.
This last one suggests a net ozone enhancement over the part of the Mediterranean Sea located south of France, which is associated with the reduction of We can also notice that simulations overestimate the ozone enhancements over Germany and England and underestimate the ones over northern Italy, as compared to the two observational datasets. This can be partly attributed to the assumed homogenization of the lockdown conditions for these countries, whereas actual restrictions in Germany and England were less strict than in Italy. On the other hand, we notice clear differences in the simulated changes associated with the pandemic lockdown for model-derived concentrations integrated up to 3 km of altitude (LMT) and also when smoothing with IASI+GOME2 averaging kernels (Fig. 5d ). In these two last cases (see Table  3 ), CHIMERE only simulates a weak reduction of ozone over all of Europe and ozone enhancements become negligible.
The range of variability of simulated concentrations decreases by more than a factor of 10, although the correlation with respect to IASI+GOME2 data remains fair (around ∼ 0. 5). This suggests a clear underestimation of the amplitude of the effect of the pandemic lockdown simulated at atmospheric layers above the surface and within the LMT (< 3 km) as compared to IASI+GOME2 satellite observations.
Discussions A fair qualitative consistency is observed between observations (in situ and satellite) and CHIMERE datasets with respect to the regions of ozone enhancements and reductions associated with the pandemic lockdown conditions. Ozone increased over more urbanized regions with large NO x emissions, such as central northern Europe and the Po Valley, and mainly decreased far from NO x hotspots where night-time titration plays a less important role in the surface ozone daily balance. The significant model/observation difference over eastern Germany and Poland might be linked to the difficulty to accurately simulate neutral chemical regimes. There, an accurate simulation of competing terms of ozone production and sinks requires a larger precision of the emission inventories and other factors affecting the abundance of active chemical species.
Conclusions We have presented a comprehensive analysis using in situ and satellite measurements of ozone as well as chemistrytransport modelling tools of the changes in ozone pollution over Europe associated with the COVID-19 pandemic lockdown of springtime 2020. To the authors' knowledge, this is the first time that satellite direct observations of lowermost tropospheric ozone are used in such a multi-data analysis. While satellite observations of ozone show a fairly good agreement with respect to in situ surface measurements of ozone across Europe in the regions where both are available, only satellite data provide full horizontal coverage both over land and ocean (at 0. 25 • × 0. 25 • horizontal resolution). The observations quantify the changes of ozone pollution in 1-15 April 2020 with respect to the same period the previous year, thus the 15 d period when the lockdown measures modified the most anthropogenic activities over the continent.
An additional original aspect in the present analysis is the adjustment of both in situ and satellite observations for estimating the impact of the pandemic lockdown on ozone pollution, using measurements from 2020 and 2019. This adjustment is derived from the chemistry-transport model CHIMERE using the meteorological conditions of each year but the same standard (business-as-usual) anthropogenic emissions. This method relies on the model accuracy in stan-dard conditions, and it neglects possible feedbacks between meteorological conditions and photochemical regimes. The influence of biogenic emissions is accounted for by the method, as these sources are derived according to the meteorological conditions. The adjustment can be directly applied to both in situ and satellite data, with good consistency with respect to other independent approaches used for the same kind of adjustment of surface in situ data  (Ordóñez et al.
Data availability. The IASI+GOME2 ozone dataset derived from MetOp-B global measurements in near real time is publicly available at the AERIS French national data centre. It can be accessed through the AERIS IASI portal on the permanent web page https: //iasi. aeris-data. fr (IASI Portal, 2022a). The data policy is detailed in https://iasi. aeris-data. fr/data-use-policy/ (IASI Portal, 2022b). Additional IASI+GOME2 data (e. g. from other MetOp satellites) can be provided upon request to the principal investigator of the satellite data: Juan Cuesta, LISA/UPEC, cuesta@lisa. ipsl. fr.
The uncertainties in the simulated NO 2 concentrations are partly linked to the inventories used by the model. The CHIMERE model used here is based on emission inventories estimated for 2010 (by HTAPv2. 2, on a monthly basis). A sustained negative trend for NO 2 concentra- tions observed over Europe between 2010 and 2017 (e. g. Pazminio et al. , 2021) suggests a positive bias for this inventory. This trend is ∼ 30 % in France, slightly higher in Italy or Belgium, and smaller for other coun- tries such as Germany and Poland (∼ 13 %; see EMEP database, https://www. ceip. at/webdab-emission-database/ emissions-as-used-in-emep-models, last access: 1 April 2022).
PADI-web (Platform for Automated extraction of animal Disease Information from the web) is a biosurveillance system dedicated to monitoring online news sources for the detection of emerging animal infectious diseases. PADI-web has collected more than 380,000 news articles since 2016. Compared to other existing biosurveillance tools, PADI-web focuses specifically on animal health and has a fully automated pipeline based on machinelearning methods. This paper presents the new functionalities of PADI-web based on the integration of: (i) a new fine-grained classification system, (ii) automatic methods to extract terms and named entities with textmining approaches, (iii) semantic resources for indexing keywords and (iv) a notification system for endusers. Compared to other biosurveillance tools, PADI-web, which is integrated in the French Platform for Animal Health Surveillance (ESA Platform), offers strong coverage of the animal sector, a multilingual approach, an automated information extraction module and a notification tool configurable according to end-user needs.
Machine learning-based classification Other systems (i. e. HealthMap, BioCaster, Argus, GPHIN) rely on supervised machine learning classifiers, namely, three Bayesian algorithms and support vector machines (SVMs). The classifiers are trained on manually labeled data and automatically learn rules to label unclassified news articles. HealthMap uses a Bayesian machine learning algorithm. GPHIN automatically computes a relevance score for each retrieved report. This score corresponds to the confidence estimate of the SVM classifier  [20]. Relevant articles in Argus were identified by keyword matching (with a set of concepts and keywords relevant to infectious disease surveillance) combined with Bayesian software tools. Experts further evaluate the automatic classification with GPHIN, HealthMap and Argus systems.
This approach is designed for sequential data; CRFs predict the probability of an output sequence according to an input sequence  [28]. The classification approach is particularly suitable for misspelt locations or short texts in terms of length, such as tweets, for which gazetteer lookups suffer from low precision due to irrelevant matches  [29]. While classifier-based approaches achieve good results, they are limited to the given categories used for the training steps. Recent tools allow users to add new types of entities to NER algorithms by training the model on annotated datasets, such as the neural network-based NER algorithm from the SpaCy package  [30]. Locations are also prone to another level of ambiguity that occurs when several distinct places have the same name, i. e. the geo/geo ambiguity, or referent ambiguity.
Data collection PADI-web retrieves articles daily from the news aggregator Google News through customized RSS feeds. An RSS feed is a combination of terms (disease names, symptoms or hosts). These terms have been identified by an approach combining text mining and domain experts. The RSS feeds are of two types: • Disease-based RSS feeds consist of disease names (e. g. , bluetongue OR BTV) and target seven animal diseases. • Symptom-based RSS feeds include clinical signs and hosts (e. g. , fever AND pigs) without any disease names. These feeds enable the detection of diseases that are yet to be monitored by PADI-web, as well as unknown diseases  [33]. RSS feeds are implemented in 16 languages (e. g. English, Chinese, Arabic, Italian, French, Russian, Turkish, etc. ).
Data processing To avoid duplicates, PADI-web checks if collected articles already exist in the database based on their URL. The webpages of the news articles that were not duplicates are visited to retrieve their content. The BeautifulSoup 1  and readability-lxml 2  Python libraries are used to collect the content of webpages and remove irrelevant elements (e. g. , pictures, hyperlinks, etc. )  [34]. Used as a piece of the preprocessing task of the PADI-web pipeline, these libraries allow us to isolate and work on the cleaned content of web documents from the web, i. e. only the body, titles, etc. All news articles that are not in English are translated using the Translator API of the Microsoft Azure system  [35].
The sentences are represented with a classical vector space model (i. e. bag-of-words). To learn the models and automatically classify sentences, we need to provide labeled data (i. e. training data). For the proposed fine-grained classification, a dedicated corpus has been built  [40]. This one is summarized below. A detailed version of the guidelines and the labeled corpus are publicly available in a Dataverse repository  [40]. For each sentence, two labeling approaches are proposed: event and information types. The Event type label aims to differentiate sentences referring to the current/recent outbreak (Current event and Risk event) from sentences referring to old outbreaks (Old event) or general information (General). Sentences that do not contain any epidemiological information are considered irrelevant (Irrelevant).
Named entity recognition As described in Section 2. 2, several tools exist for named entity recognition (NER). SpaCy has been integrated into PADI-web 3. 0. SpaCy already includes powerful NER models that allow recognizing general named entities in texts using several languages. With PADI-web, we can use a classical model to identify well-known named entities such as locations and organizations. Moreover, specific models for entity recognition for the animal disease surveillance domain (e. g. , host, etc. ) could be used (see Section 2. 2). We have used a labeled dataset  [41]  to learn and integrate a domain-specific model, which is able to detect host and disease names, as well as numbers of cases related to an outbreak. Both types of entities (i. e. general and specific) are then recognized with PADI-web 3.
Named entity annotation To annotate textual data, Brat 4  was integrated into PADI-web 3. 0. Brat is a powerful annotation tool that contains all the needed functionalities to prepare and annotate a corpus (see Fig. 4 ). Moreover, PADI-web includes the possibility to export texts from the PADI-web database and convert them into the Brat format. PADI-web also includes the option to use the Brat annotated corpora to update an existing model and update it with new examples.
Terminology extraction and semantic resource Terminology extraction is an important task in the natural language processing (NLP) domain. This enables the extraction of relevant and discriminative terms in textual data. Into PADI-web 3. 0, we integrated a tool called BioTex  [43]  to highlight terms extracted from the textual data (i. e. news) of PADI-web. We use BioTex because it was initially built for the medical domain. BioTex combines linguistic and statistical information adapted to biomedical areas. To select the appropriate terms, BioTex uses two principles: (i) a combination of statistical methods, e. g. , term frequency-inverse document frequency (TF-IDF), Okapi BM25 and C-value measures  [43] ; and (ii) use of a list of syntactic structures of the terms that have been learned with relevant sources in the medical domain, e.
g. , MeSH (Medical Subject Heading). The terms extracted with BioTex can be either words (e. g. , 'pig') or multiword terms (e. g. , 'wild pig', 'domestic pig'). To index data dealing with the agriculture domain, the AGROVOC 5  thesaurus has been integrated into PADI-web 3. 0. AGROVOC is the largest linked open dataset dealing with the agriculture domain. AGROVOC is a thesaurus that contains 38,780 concepts and 808,000 Fig. 6. Extract of a notification received the 8th of April 2021 related to avian influenza disease -List of new articles collected with French (FR) (automatically translated in English) and English (EN) feeds.
terms. Each keyword source is in one of 3 different source types that PADIweb 3. 0 handles: from semantic resources (i. e. , AGROVOC), from users (e. g. , epidemiological-related keywords provided by a user), and from automatic processes (i. e. , SpaCy and BioTex). Keywords are organized according to their source type and have a different color depending on it. In the text body, icons standing for keyword matches follow the same color code as in lists of keywords (see Fig. 5 ).
Linking Finally, a keyword alignment mechanism is integrated into PADIweb 3. 0 for entity and keyword recognition with elements of the PADI-web database. This keyword alignment mechanism is based on the Levenshtein distance  [44, 45] : two keywords are aligned if their distance is below a user-specified threshold. The distance measure is a   normalized Levenshtein distance (normalized between 0 and 1 by dividing it by the length of the longest string). The Levenshtein distance between two strings is given by the minimum number of operations needed to transform one source string into the target string. An operation is an insertion, deletion, or substitution of a single character. The following subsection presents a notification system that uses the new functionalities presented in Subsections 3.
Precision corresponds to the number of relevant sentences of a given class over the number of sentences attributed to this class. Recall is the number of relevant sentences of a given class over the real number of relevant sentences associated with this class. The F-measure is the harmonic mean of precision and recall. We presented the results using an MLP classifier in Tables  1  and 2. Classification scores for some classes are better (e. g. , Current event, Descriptive epidemiology) than other ones (e. g. , Old event, Distribution). We could explain this situation with the unbalanced datasets used. Moreover, during the annotation phase, many instances of dedicated classes (e. g. , general epidemiology) involved the same sentence structure (e. g. , 'The virus causes a hemorrhagic fever with high mortality rates in pigs') that favors machine learning approaches.
MLP and SVM achieved comparatively equal performances and outperformed the NB classifier  [48]. These behaviors were identical for both event type and information type classifications  [48]. Into PADI-web 3. 0, a selection of model families is trained on the current dataset (random forests with various parameters, linear support vector classification, neural networks, Gaussian-based models, K-nearest neighbors, etc. ), using a 5-fold cross-validation scheme. The model obtaining the highest mean accuracy score  6  is used to classify each new retrieved sentence. For article-level classification tasks, a model is trained and built automatically every night using existing user classification labels. Currently, the trained classification models integrated into PADI-web 3. 0 reach a mean accuracy score of 0.
Acknowledgements This work was funded by the  French General Directorate for Food (DGAL) , the  French Agricultural Research Centre for International Development (CIRAD)  and the  SONGES Project (FEDER and Occitanie). This work was also supported by the  French National Research Agency  under the Investments for the Future Program, referred to as  ANR-16-CONV-0004. We thank  Valérie De Waele  and  Aline Vilain  ( Moriskin project coordinated by Sciensano  ( Institute of Public and Animal Health) -Belgian Federal Public Service Health ) for the annotation task of the corpus cited in this paper. This study was partially funded by  EU  grant  874850 MOOD  and is catalogued as MOOD014. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission.