dataesr/scientific-paragraphs-categorization huggingface dataset:
The thematic maps of these landslide-influencing factors of LX and GG are shown in Figures  2  and 3 , respectively. A landslide survey is an indispensable procedure for data statistics and understanding of landslide spatial distribution  [22]. The landslide inventories of the study areas were initially obtained from the archive database in 2012 and then were supplemented with several field surveys using geoinformatics (ArcGIS) from Google Earth images (historical images from 2010 to 2016). Additionally, the description of the landslides in the study areas can be found in Table  A1  of Appendix A. Figure  1  shows the landslide distribution of the first two study areas. The inventory contains 484 landslide locations in LX and 88 landslide locations in GG. These historical landslide locations are explored by using expert knowledge and onsite investigation, which can be found in the report by the China Geological Environment Monitoring Institute  [23].
Distance to faults This has an important influence on the distribution and scale of landslides in the study area  [30]. The data on the locations of faults in the study area were obtained from the National 1:200,000 Digital Geological Map (Public Edition) Spatial Database of China  [41] , and The Euclidean distance tool of ArcGIS was used to obtain the fault distance in the study area. In addition to the positive samples, the same number of negative samples (nonlandslide) are randomly collected from the first two study areas on the basis of following some basic priorities (for example, low slopes)  [11, 16]. The heat maps of the correlation matrix of the landslide-influencing factors and one output variable for data sets in three study In addition to the positive samples, the same number of negative samples (nonlandslide) are randomly collected from the first two study areas on the basis of following some basic priorities (for example, low slopes)  [11, 16].
Data Reconstruction and Transfer Learning with Attributes Reconstructing data with attributes can make data with specified knowledge, thereby increasing the commonality and representativity of samples  [20]. Specifically, a VAEGAN was pretrained by using the data set of LX and the one of GG. Additionally, the latent vector representations of each data set will be extracted by the pretrained VAEGAN. That is, for each attribute, the mean vectors of the latent space of landslide-influencing factors in each area are computed. Next, the attributes that existed only in a study area (e. g. , GG) will be obtained by computing the difference between the mean vectors (operation [a] in Figure  6 ). Finally, the data containing the attributes of two areas will be obtained by reconstructing the vector, which is the integrated result of the attribute vector (with attributes) and the latent space of LX (without attributes) (operation [b] in Figure  6 ).
The reconstructed data contain the attributes of the landslide-influencing factors in GG and LX, which makes the LSP model learn the attributes beyond one study area. Additionally, the TLA will then be achieved by fine-tuning the model pretrained by the reconstructing data with the labeled samples of GG (Figure  7 ). Land 2023, 12, x FOR PEER REVIEW 10 of 28 was pretrained by using the data set of LX and the one of GG. Additionally, the latent vector representations of each data set will be extracted by the pretrained VAEGAN. That is, for each attribute, the mean vectors of the latent space of landslide-influencing factors in each area are computed. Next, the attributes that existed only in a study area (e. g. , GG) will be obtained by computing the difference between the mean vectors (operation [a] in Figure  6 ).
Finally, the data containing the attributes of two areas will be obtained by reconstructing the vector, which is the integrated result of the attribute vector (with attributes) and the latent space of LX (without attributes) (operation [b] in Figure  6 ). The reconstructed data contain the attributes of the landslide-influencing factors in GG and LX, which makes the LSP model learn the attributes beyond one study area. Additionally, the TLA will then be achieved by fine-tuning the model pretrained by the reconstructing data with the labeled samples of GG (Figure  7 ). was pretrained by using the data set of LX and the one of GG. Additionally, the latent vector representations of each data set will be extracted by the pretrained VAEGAN. That is, for each attribute, the mean vectors of the latent space of landslide-influencing factors in each area are computed.
Next, the attributes that existed only in a study area (e. g. , GG) will be obtained by computing the difference between the mean vectors (operation [a] in Figure  6 ). Finally, the data containing the attributes of two areas will be obtained by reconstructing the vector, which is the integrated result of the attribute vector (with attributes) and the latent space of LX (without attributes) (operation [b] in Figure  6 ). The reconstructed data contain the attributes of the landslide-influencing factors in GG and LX, which makes the LSP model learn the attributes beyond one study area. Additionally, the TLA will then be achieved by fine-tuning the model pretrained by the reconstructing data with the labeled samples of GG (Figure  7 ).
The emergence of powerful natural language Artificial Intelligence systems, such as ChatGPT 4. 0, has created a new dynamic in schools, colleges and universities. Some see the present moment as a turning point in our thinking about teaching, learning and assessment, with new approaches and practices suddenly becoming possible. Others are concerned that unleashing powerful AI tools on an unready education system may distort and damage a fragile system. AI tools can be used to advance the agenda of technification and the global education reform movement (GERM) or can be used to enable a mindset shift to a more engaged, authentic, compassionate, and collaborative approach to teaching and learning. This paper explores this moment of truth.
The "Big Bang" In November 2022, the company OpenAI launched Version 3 of ChatGPT. Within weeks it had secured over one hundred million users, becoming one of the most widely adopted software applications in the shortest time-span in the world. While some were taken aback by what this technology could do -they developed "AI-anxiety" -others embraced it as heralding a new era in the deployment of "human-like" tools to support the work of lawyers, teachers, health care workers, video creators and others. ChatGPT was not new. The first version was launched in 2018, with new versions following each year. What is different about v3. 5 versus v1 is the knowledge base it uses to respond to users' questions. ChatGPT 1 was "trained" on 117 million parameters. The current version (ChatGPT 4 released in 2023) was "trained" with over one trillion parameters as well as live access to the internet.
Over time ChatGPT is more accurate, produces fewer "nonsense" responses, has reduced bias and has accessed more information in more languages from more parts of the world. Microsoft, which paid US$10 billion to be able to integrate ChatGPT and other AI products developed by OpenAI, will now integrate these large language learning (LLL) modelbased AI systems into all of its Office products with what is known as "co-pilot. " This permits users to write documents in Word, develop PowerPoint slide decks or analyze data in Excel to access a 24x7 digital assistant to accelerate workflow and reduce the complexity of these tasks. Some have expressed deep concern about the emergence of such AI systems as ChatGPT, suggesting that there was a need to "pause" AI developments while the legal, ethical, cybersecurity and bias concerns were more fully understood.
AI and Technification Artificial Intelligence (AI) enables learning, assessment, research, creativity, and collaboration. Its emerging presence is focused on: • Learning Analyticsusing algorithms and datasets to predict student behaviour and engage in early intervention to support retention and completion. A short catalogue of specific tools is provided in Appendix 1. All of these systems rely on significant volumes of data, both to start their work and to maintain it. Some AI systems learn from their interactions with humans and can "appropriate" information and knowledge to improve reliability and accuracy. All use certain kinds of algorithms to do their work and perform poorly if the evidence-based the algorithms are based on is narrow and small (e. g. , a highly specialized knowledge domain with less than 10,000 reference points).
2016 ; Planck Collaboration 2016a ). Moreo v er, further analyses of these and other datasets sho w increasing e vidence of complexity in the spectral and spatial behaviour of the Galactic dust and synchrotron emissions  (Choi & Page 2015 ; Planck Collaboration 2017a ; Krachmalnicoff et al. 2018 ; Fuskeland et al. 2021 ; de Belsunce, Gratton & Efstathiou 2022 ; Weiland et al. 2022  ). The situation is particularly complex for the polarized synchrotron emission. The sensitivity of the low-frequency channels from Planck and WMAP does not allow the detection of polarized synchrotron signal at intermediate and high-Galactic latitudes, and therefore we are lacking a detailed spectral modelling of this emission precisely in the regions of cosmological interest. In this context, there is a need for complementing the existing satellite observations with measurements at lower frequencies in order to impro v e our description of the foregrounds at the required level for B-mode studies.
A comparison of these six null test power spectra provides a useful tool to identify and isolate different sources of systematic effects or calibration errors. In polarization, all null test spectra are basically consistent among them, except the ring case, which presents a slightly lower level of 1/ f residuals at low multipoles. This behaviour is expected because the ring null test maps probe noise variations in scales of one minute, while the others cases (half, daynight, tbem, pwv) probe longer time scales. We also note that the halfring null test tends to be slightly abo v e the other noise estimates, but again this is expected as this null test uses basically half of the possible crossings for each pixel, and thus the baseline solution is less constrained. Ho we ver, this is not the case of halfring null test with common baselines, as in this case the baseline solution was obtained with the complete dataset.
6  ). Later, in the post-processing stage (Section 2. 4 ), any residual RFI signals emerging after co-adding all data in the map-making process are corrected using a function of the declination. In terms of relative amplitude, this is by far the largest correction applied to the MFI wide-surv e y polarization data, with its amplitude being higher in the 11 and 13 GHz channels due to the emission of geostationary satellites entering through the far sidelobes. Indeed, the ef fecti ve transfer function of the MFI wide surv e y in polarization is mainly determined by this effect (see Section 2. 5 ). In order to quantify the relative importance of these three corrections, and to e v aluate the possible impact of any residual systematic Table  15. Average inter-channel correlations <ρ > of the half-mission difference maps between the correlated and uncorrelated channels for a given horn and frequency.
Polarization efficiency As discussed in Section 2. 6 , the calibration of the polarization efficiency of the MFI wide survey data is done in two steps. First, we use laboratory measurements taken at the end of period six to calibrate the polar efficiency of each individual MFI channel. In addition, we use the wide surv e y data in period six to add also the correction factors to these polar efficiencies associated with a possible error in the determination of the r -factors. These procedures provide a determination of the polarization efficiency in period 6 with a relativ e accurac y of 2 per cent. Then, in a second step these values from period six are transferred to the other two periods that are used in the construction of the MFI wide surv e y polarization maps (i. e. 2 and 5), using beam fitting photometry (BF1d) measurements on Tau A.
Internal calibration of the wide sur v ey and consistency checks: evaluating unknown systematics Following the methodologies outlined in Planck Collaboration ( 2014c , 2014d ), we use internal consistency checks based on null test maps and other data splits of the wide surv e y in order to estimate the impact of systematic effects in the o v erall calibration. This is particularly useful for assessing the impact of 'unknown systematics', i. e. those for which we do not have specific measurements or numerical simulations. For the MFI wide survey, and given that we want to focus on the relative calibration of the instrument, we use as a reference the set of null test maps and data splits labelled as 'with common baselines' in Section 4. 1.
The sign of b distinguishes entrainment and deposition. The crux of the matter rests on how we should be computing the term b. For earlier pioneering work using Saint-Venant solvers, b was set to have a constant value for the entire duration of flows  [2, 10]. Whilst this was a fine starting point, solvers were essentially blind to the local underlying topography. More recently, the term b has been adapted to account for empirical entrainment relationships (see  [12] [13] [14]  for examples of such relationships, and  [15]  for an implementation). Although this represents a logical extension of earlier works, empirical relationships tend to be site-specific, and in any case rely on the availability of statisticallysignificant and well-characterised datasets. It is worth noting that in principle, it should be possible to derive an expression for b from the rheologies of the flow and the bed, thus circumventing the need for ad-hoc relations.
Eventbased surveillance (EBS) systems, such as HealthMap or GPHIN, have thus been developed to collect and process the continuous flow of informal information. Such systems increasingly marshal text-mining methods to alleviate the amount of manually curated free text  (Hartley et al. , 2010). Text mining, which combines natural language processing (NLP) and data mining techniques, enables free text conversion into a computer-readable format  (Hearst, 1999). The pipelines of EBS systems include four steps, from data collection to the extraction of relevant pieces of epidemiological information (Fig. 1 ). Each step can be performed manually, semiautomatically or automatically according to the system. In the animal health domain, the final extraction of epidemiological information (or entities) aims at identifying, among others, the disease names, species, locations, and dates related to a specific outbreak.
Before this step, a classification step is usually performed to filter the relevant documents (e. g. , online news). Classification is a crucial step of animal disease surveillance systems, as it avoids overwhelming the systems with irrelevant data (e. g. , a review about a disease). The classification approaches integrated into the EBS systems differ regarding the number of categories used to label the documents, the type of classification method, and the kind of moderation. Several tools, such as HealthMap, GPHIN and PADI-web  (Brownstein et al. , 2008; Carter et al. , 2020; Valentin et al. , 2020b) , rely on classifiers trained on manually labeled datasets to automatically learn rules to label the retrieved news articles (so-called supervised classification). The classifiers include, for instance, naïve Bayes, the support vector machine (SVM), and more recently, deep-learning classifiers such as bidirectional long short-term memory recurrent neural networks  (Kim et al.
The second annotation level characterizes the relevant sentences' epidemiological topic (fine-grained information), as shown in Table  1. In this study, we adopted the supervised classification paradigm using the previously described corpus of sentences as the training dataset. The two levels form two consecutive classification tasks: i) classification of the relevance status and ii) topic classification of the relevant sentences (Fig. 2 ). The first classification is binary (sentences are either relevant or irrelevant), while the sentence is multiclass (i. e. , sentences belong to one of the six topic categories). To evaluate retrieval methods on sufficient class sizes, we increased the initial annotated corpus (32 news articles, 486 sentences -10,247 words) with 758 sentences (16,417 words).
Contrary to BOW, word embedding representations are obtained by a learning process on a large corpus. This learning process is distinct from subsequent NLP tasks for which embeddings are used, namely, text classification in our study. Several pretrained word embeddings are publicly available, but training a word embedding model on text specific  to the target domain has been shown to improve performance  (Pyysalo et al. , 2015). In this study, we thus decided to compare a pretrained model (w2v-G) with different custom-trained models (w2v-P1, w2v-P2, and w2v-P3), whose characteristics are summarized in Table  2. Two types of parameters can influence the quality of vectors when training a word embedding model: (i) the preprocessing steps of the training dataset and (ii) the model parameters.
, , 2013a;; Pennington et al. , 2014). For simplicity, both dimensions are further referred to as "100" and "300". We used the default parameter for the window size (5 words). We trained the custom models on a dataset of news articles dealing with the animal health domain. This dataset is called Epi-Animal. The training set length was 33,417,501 words. The word2vec pretrained model was trained on a 3 billion-word corpus from the Google News corpus 2  with the CBOW architecture. Each sentence vector was computed by calculating the average of the sentence's word embeddings. As proposed by  (De Boom et al. , 2016; Krzeszewska et al. , 2022) , each word embedding was leveraged by the TF -IDF word value. Finally, each sentence is represented by a vector that pools the information of all of the words.
, 2018; Mandelbaum and Shalev, 2016). We used the default parameters implemented in the scikit-learn library  (Pedregosa et al. , 2011) , i. e. , 1 hidden layer, 100 hidden units and a rectified linear unit (ReLU) activation function. 3. As this study focuses on the evaluation of the textual representation, we did not fine-tune the classifiers. We are nevertheless aware that fine-tuning can improve the performances of each classifier, and our result must be interpreted in the context of the set of parameters cited above. Preprocessing, word embedding model training, classification and the evaluation pipeline were performed using the scikit-learn, NLTK and gensim libraries (Python)  (Pedregosa et al. , 2011; Bird and Loper, 2004; Rehur and Sojka, 2010). The code is freely available at: https://github.
Discussion The classification results based on both BOW and word embedding representations showed that the supervised approach performed well in identifying relevant sentences in animal-health-related news (with the highest weighted F-measure values reaching 0. 839 and 0. 871, respectively). While trained on approximately the same dataset lengths, the performance of document-based classification reported by the EBS system is higher (F-measure of 0. 93 for GPHIN  (Conway et al. , 2009) , accuracy of 0. 92 for PADI-web on an external dataset  (Valentin et al. , 2020a) ). Sentence classification is more challenging due to sentence sparsity. The classification of epidemiological topics achieved poorer global performances, with the underrepresented classes obtaining the lowest F-measures. This can be explained by the multiclass classification task, which may require richer semantics.
Moreover, the topic classification includes underrepresented and low-frequency classes, which trigger classification errors in a supervised framework. This can be a major limitation in practice when retrieving underrepresented classes, such as a transmission pathway or concern and risk factors. Our results weakly suggest that training on word embeddings may overcome this limitation by learning more effectively the semantic meaning of short texts. Embedding models are trained on external datasets, which is contrary to traditional bag-of-words representations. They thus allow the classifiers to generalize more effectively beyond their limited number of training examples  (Thapen et al. , 2016). Such a feature of word embedding models is of utmost interest when implementing supervised approaches in a specialized domain, such as animal disease surveillance.
The model trained on the Epi-Animal corpus achieved slightly better results than the pretrained word2vec model, suggesting that a specialized corpus had a greater value than training on a larger generalist corpus. Further evaluation is needed to validate this result. For relevance classification, the use of word embeddings did not strongly outperform bag-of-words representations. Previous studies have suggested that word embeddings with traditional classifiers and a sufficient learning dataset size may not have additional value for classification tasks (d  'Amato et al. , 2017; Bollig et al. , 2020). In  Bollig et al. (2020) , the GloVe embedding model did not outperform the TF-IDF vector model in classifying necropsy reports. Compared to our study, the model was trained on a smaller dataset (1000 reports, forming a 50, 000-word vocabulary).
Although these approaches cannot be directly compared since the word embedding models were different, we may hypothesize that the added value of word embeddings also depends on the sparsity of the data classified, i. e. , short text such as sentences or longer documents such as reports or news articles. In our results, none of the word2vec training parameters (i. e. , architecture and vector length) significantly outperformed the other parameters. Skip-gram is known to be more efficient, with infrequent words and small training datasets  (Naili et al. , 2017). Hence, we expected this model to perform better than CBOW. The choice of embedding dimension is still an open issue in the literature. It relies on a trade-off between small dimensionality, which may not capture all possible word relations, and large dimensionality, which suffers from overfitting  (Yin and Shen, 2018).
Funding This work was funded by the  French General Directorate for Food (DGAL) , the  French Agricultural Research Centre for International Development (CIRAD). 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. This study was partially funded by  EU  grant  874850 MOOD  and is catalogued as MOOD008. The contents of this publication are the sole responsibility of the authors and do not necessarily reflect the views of the  European Commission.
Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug -target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated.