dataesr/scientific-paragraphs-categorization huggingface dataset:
Here, we let the trees fully grow until there is only one element in each leaf as usually done  (Hastie et al. , 2009 ). An optimisation on our full dataset showed that 3000 trees were sufficient (more trees do not improve the results), so this value was selected for all this study. We here have two classes, namely avalanche and non-avalanche days, that are highly unbalanced (mean of 1. 1% of avalanche days in the winter season depending on elevation and aspect, see Figure  2 ). Machine learning techniques, if not handled with care, do not perform well on unbalanced data (e. g. Hastie et al. , 2009; Sielenou et al. , 2021). They are designed to optimize the overall classification accuracy or a similar score. Their results thus tend to be biased towards the majority class  (Chawla et al. , 2004; Sielenou et al.
, 2021) , here the non-avalanche days. The most common techniques to limit this effect are oversampling https://doi. org/10. 5194/tc-2022-108 Preprint. Discussion started: 3 June 2022 c Author(s) 2022. CC BY 4. 0 License. of the minority classes, undersampling of the majority classes or dedicated learning algorithms. We here used a combination of these techniques. We only considered days of the winter season and characterized by a simulated snow depth larger than 1 cm. This first selection led to the undersampling of the majority class. However, this first step was not sufficient to fully balance the dataset. We therefore used an adaptation of RF classifier to deal with unbalanced data  (Chen et al. , 2004) : each tree of the forest is trained on a subset of the data randomly drawn; the probability law for drawing is adapted so that the probability of drawing non-avalanche or avalanche day are identical.
Therefore, the confusion matrix of 58 test years were aggregated to compute scores with all information available. This leave one year out approach is used for all evaluations presented. We also quantified the statistical uncertainty related to the sample size. As we used 58 years of evaluation data computed separately, we were able to define an uncertainty by bootstrapping evaluation years used to compute the considered score. In practice, 1000 independent draws of 58 years (with replacement) were randomly produced and the scores were computed on each draw. The 20th and 80th percentiles were used to quantify the uncertainty of the produced scores.
Importance of variables The importance of variables was estimated through the separative power of each variable in the trees by computing the normalized mean decrease of impurity (also called Gini importance) on nodes where the given variable is used to separate the data in two groups  (Breiman, 2001). A variable importance of zero means that the variable could be removed without reducing model performance and a high value denotes a high separative capacity (between avalanche and non-avalanche days) of the variable. If two variables contain similar information, each variable will be picked randomly in the tree construction and these variables Table  4. Confusion matrix for the test dataset: observed and predicted avalanche days ("Avalanche") and non-avalanche ones ("Nonavalanche") summed over elevation and aspect ranges.
Other pro's and con's of the approach We here used the EPA as the ground truth of avalanche activity. This dataset is unique in its spatial and temporal extension but mainly focuses on large avalanches reaching valley floors or infrastructures. In consequence, the high-elevation avalanche activity and smaller avalanches are not reported which leads to a limited number of avalanche days in the dataset. For the spatiotemporal domain selected in this study  (Haute-Maurienne, 1960 -2018) , the number of avalanche events reported in the EPA remains large enough (2518 avalanche situations) but in other regions, the scarcity of reported avalanche events might become a problem. Moreover, this study presents a binary classification as there is rarely more than one avalanche per day and spatial unit (aspect-elevation), which limits the definition of several classes of avalanche activity.
Conclusion and outlooks This paper combines snow cover modelling, mechanical stability indices and observational data through machine learning for avalanche prediction. In particular, we considered numerous stability indices and their time-derivatives. To evaluate the random forest model, we define a robust method adapted to the specific behaviour of the snowpack (long-term memory). This evaluation was conducted on three district municipalities of the French Alps on 58 years of a comprehensive dataset of avalanche observations, with a high spatial resolution (8 aspects and 3 elevation ranges) and an extended set of variables describing both meteorological, mechanical stability variables and their time evolution. The combination of snow physics through snowpack modelling, stability indices and their derivatives, with random forest proves to be valid.
We study the problem of query evaluation on probabilistic graphs, namely, tuple-independent probabilistic databases over signatures of arity two. We focus on the class of queries closed under homomorphisms, or, equivalently, the infinite unions of conjunctive queries. Our main result states that the probabilistic query evaluation problem is #P-hard for all unbounded queries from this class. As bounded queries from this class are equivalent to a union of conjunctive queries, they are already classified by the dichotomy of  Dalvi and Suciu (2012). Hence, our result and theirs imply a complete data complexity dichotomy, between polynomial time and #P-hardness, on evaluating homomorphism-closed queries over probabilistic graphs. This dichotomy covers in particular all fragments of infinite unions of conjunctive queries over arity-two signatures, such as negation-free (disjunctive) Datalog, regular path queries, and a large class of ontology-mediated queries.
Introduction The management of uncertain and probabilistic data is an important problem in many applications, e. g. , automated knowledge base construction [DGH + 14, HSBW13, MCH + 15], data integration from diverse sources, predictive and stochastic modeling, applications based on (error-prone) sensor readings, etc. To represent probabilistic data, the most basic model is that of tuple-independent probabilistic databases (TIDs)  [SORK11]. In TIDs, every fact of the database is viewed as an independent random variable, and is either kept or discarded according to some probability. Hence, a TID induces a probability distribution over all possible worlds, that is, all possible subsets of the database. The central inference task for Second, we focus on all other unbounded queries in UCQ ∞ , i.
Related Work Research on probabilistic databases is a well-established field; see e. g. [SORK11]. The first dichotomy for queries on such databases was shown by Dalvi and Suciu  [DS07] : a self-join-free conjunctive query is safe if it is hierarchical, and #P-hard otherwise. They then extended this result to a dichotomy for all UCQs  [DS12]. Beyond UCQs, partial dichotomy results are known for some queries with negation  [FO16] , with disequality ( =) joins in the queries  [OH08] , or with inequality (<) joins  [OH09]. Some results are known for extended models, e. g. , the dichotomy of Dalvi and Suciu has been lifted from TIDs to open-world probabilistic databases  [CDV21]. However, we are not aware of dichotomies in the probabilistic database literature that apply to Boolean queries beyond first-order logic, or to queries with fixpoints.
Query evaluation on probabilistic databases has also been studied in restricted contexts, e. g. , when probabilistic tuples are only allowed to have probability 0. 5. This is for instance the focus of the recent paper of Kenig and Suciu  [KS21] , which we discuss in Section 8. Query evaluation on probabilistic graphs has also been studied in the context of ontologymediated queries (OMQs)  [JL12, BCL17, BCL19 ]. An OMQ is a composite query that typically consists of a UCQ and an ontology. The only classification result on PQE for OMQs beyond first-order-rewritable languages is given for the description logic ELI  [JL12]. This result applies to a class of queries that go beyond first-order logic. Our work generalizes this result (Theorem 6 of  [JL12] ) by showing hardness for any unbounded UCQ ∞ , not just the ones expressible as OMQs based on ELI.
Preliminaries In this section, we introduce all technical preliminaries relevant to our study. In particular, we introduce the query languages studied in this paper, and the tuple-independent probabilistic database model. We also discuss briefly the complexity classes relevant to our study, as well as two canonical #P-hard problems which are used later in the reductions. Vocabulary. We consider a relational signature σ which is a set of predicates. In this work, the signature is required to be arity-two, i. e. , it consists only of predicates of arity two. Our results can easily be extended to signatures with relations having predicates of arity one and two, as we show in Section 8. A σ-fact is an expression of the form F = R(a, b) where R is a predicate and a, b are constants. By a slight abuse of terminology, we call F a unary fact if a = b, and a non-unary fact otherwise.
A σ-atom is defined in the same way with variables instead of constants. For brevity, we will often talk about a fact or an atom when σ is clear from context. We also speak of R-facts or R-atoms to specifically refer to facts or atoms that use the predicate R. It will be convenient to write σ ↔ the arity-two signature consisting of the relations of σ and of the relations R -for R ∈ σ, with a semantics that we define below. Database instances. A database instance over σ, or a σ-instance, is a set of facts over σ. All instances considered in this paper are finite. The domain of a fact F , denoted dom(F ), is the set of constants that appear in F , and the domain of an instance I, denoted dom(I), is the set of constants that appear in I, i. e. , the union of the domains of its facts. Every σ-instance I can be seen as a σ ↔ -instance consisting of all the σ-facts in I, and all the facts R - If we say that we create a fact R(a, b) for R ∈ σ ↔ , we mean that we create S(a, b) if R = S for some S ∈ σ, and S(b, a) if R = S -for some S ∈ σ.
An OMQ is a pair (Q, T ), where Q is (typically) a UCQ, and T is an ontology. A database instance I satisfies an OMQ (Q, T ) if the instance I and the logical theory T entail the query Q in the standard sense -see, e. g. ,  [BCLW14] , for details. There are ontological languages for OMQs based on description logics [BCM + 07] and on existential rules, also known as tuple-generating dependencies (TGDs)  [CGK13, CGL12]. It is well known that every OMQ (Q, T ) ∈ (UCQ, TGD) is closed under homomorphisms. Thus, the dichotomy result of the paper applies to every OMQ from (UCQ, TGD) over unary and binary predicates, which, in turn, covers several OMQ languages based on description logics. There are also many OMQs that can be equivalently expressed as a query in Datalog or in disjunctive Datalog over an arity-two signature [BCLW14, EO Š+ 12, GS12], thus falling in the class UCQ ∞.
In particular, this is the case of any OMQ involving negation-free ALCHI (Theorem 6 of  [BCLW14] ), and of fragments of ALCHI, e. g. , ELHI, and ELI as in  [JL12]. Probabilistic query evaluation. We study the problem of probabilistic query evaluation over tuple-independent probabilistic databases. A tuple-independent probabilistic database (TID) over a signature σ is a pair I = (I, π) of a σ-instance I, and of a function π that maps every fact F to a probability π(F ), given as a rational number in [0, 1]. Formally, a TID I = (I, π) defines the following probability distribution over all possible worlds I ⊆ I: π(I ) := F ∈I π(F ) ×   F ∈I \I (1 -π(F ))  . Then, given a TID I = (I, π), the probability of a query Q relative to I, denoted P I (Q), is given by the sum of the probabilities of the possible worlds that satisfy the query: P I (Q) := I ⊆I,I |=Q π(I ).
e. , Actinopterygii and Cyclostomata) that have freshwater listed as one of their habitats in FishBase  (Froese & Pauly 2022). Fish living only in brackish water and/or saltwater were not considered (e. g. , Sparus aurata). Specifically, we assess four aspects of freshwater fish invasions: 1. The pathways by which nonnative freshwater fish species are introduced around the world and their relative importance in terms of the number of established nonnative fish species 2. The characteristics of nonnative species and receiving ecosystems, which can affect the success of each stage of the invasion process 3. The main impacts and the impact mechanisms of invasive nonnative freshwater fish species and their relative importance according to the Global Invasive Species Database (GISD) (ISSG 2015) 4.
Hybridization between closely related invasive and native fish species is common due to their external mode of fertilization  (Olden et al. 2004 , Ludwig et al. 2009 , Blackwell et al. 2020). For example, in the Bar charts illustrating the relative proportion of records of (a) impact mechanisms (198 records) and (b) types of impacts (303 records) for nonnative freshwater fish. Data are taken from the Global Invasive Species Database (GISD)  (ISSG 2015). The GISD compiles impacts and mechanisms of invasive species worldwide with geographic and temporal details. Each record is defined here as the documented observation of (a) a mechanism or (b) an impact of one species in one location. The spatial resolution of records is variable; some records were recorded at the country level, whereas others were recorded at the scale of a drainage basin.
Gaps in Data Coverage There is a severe lack of data on several aspects of freshwater fish invasions, including essential aspects such as the number of invasion occurrences, particularly in specific regions that are poorly documented. For example, there are limited data on the propagule pressure of freshwater fish species (García-Berthou 2007). Only minimal figures on fish production and commerce are given by the Food and Agriculture Organization of the United Nations (Food Agric. Org. UN 2016), while statistics on ornamental fish releases are restricted to North America and based on predictive models  (Strecker et al. 2011). There is also a lack of data regarding the economic impacts of freshwater fish invasions despite the potential high costs associated with them  (Haubrock et al. 2022).
Likewise, invasive freshwater fish have not been classified in the SocioEconomic Impact Classification of Alien Taxa  (Bacher et al. 2018). The same paucity applies to their ecological impacts. While the ecological impact classification of invasive species exists for several taxa through the Environmental Impact Classification for Alien Taxa (EICAT) database  (Hawkins et al. 2015) , this classification is not available for freshwater fish species. Likewise, the only database that lists management plans implemented for invasive freshwater fish species by country is the GISD. However, this database is incomplete, as it contains management information for only 27 nonnative freshwater fish species in 14 countries, even though at least 551 nonnative freshwater fish are established worldwide (Figure  1 ).
For example, some iconic invasive species with known management plans are not included in the database (e. g. , sea lamprey). Previous studies such as that by  Rytwinski et al. (2019)  have already raised the issue of poorly documented evaluations of eradication methods. However, several other databases reporting information on freshwater fish invasions do exist [e. g. , economic impacts  (Diagne et al. 2020) , occurrence in drainage basins  (Tedesco et al. 2017)  Nevertheless, these databases do not cover all the aforementioned data gaps. They are also affected by unquantified incompleteness biases, which necessarily affect predictions and conclusions relating to freshwater fish invasion patterns.
As the characteristics of recipient ecosystems (e. g. , climatic conditions) are an important factor influencing invasions, the accumulated knowledge on North American species is not representative of invasive species in other regions of the world. In particular, we know that the African region is heavily exposed to invasive freshwater fish, but studies are still severely lacking in this region  (Pyšek et al. 2020 , Haubrock et al. 2022). Furthermore, the available databases on invasions are not updated within a sufficient timeframe to allow for real-time monitoring of invasions. For example,  Tedesco et al. (2017)  list only a few introductions in the Amazon drainage basin, even though recent reports show an increase in invasions  (Vitule et al. 2019 , Magalhães et al. 2020). Similarly, Guianese rivers have long been considered among the most pristine, but recent reports point to introductions of several nonnative fish species that must be considered an early sign of potential invasions  (Brosse et al.
2021). These examples are not documented in the  Tedesco et al. (2017)  database, thereby preventing users from obtaining up-to-date and accurate information on invasions. This problem can even be quantified in the database of first records of established species created by  Seebens et al. (2017). Indeed, the first-record rate of nonnative established fish species declined after 2000, partly due to the detection delay  (Seebens et al. 2017). Comprehensive and up-to-date databases are therefore essential for building reliable invasion models, especially as ecosystems and the global economy are likely to face major changes in the coming years. One solution might be the development of long-term projects with sufficient funding to ensure the regular updating of such important databases.
Recommendations In this review, we provide an overview of different aspects of freshwater fish invasions, from pathways of introduction to management techniques. We also highlight several research gaps that need to be filled. Here, we provide a few recommendations on the main issues that should be addressed in future studies. First, data collection efforts should focus on specific areas (e. g. , Africa, South America) and aspects of fish invasions where data are poor or nonexistent. Comprehensive data on the propagule pressure of introduced fish could be collected and gathered in a single comprehensive database. We are aware that gathering this information is difficult, but the collection of proxies for propagule pressure such as import data or ballast water volume could also be a potential solution to fill this gap  (Drake et al.
2015). Regarding impacts, tremendous progress in research could be achieved by classifying the ecological impacts using the EICAT classification and completing the data on economic impacts with a specific focus on less-studied species and regions [e. g. , the Nile perch is known to impact local communities of fishermen in East Africa, but its costs are not recorded  (Haubrock et al. 2022) ] (Supplemental Appendix 2). In addition, it is well known that some invasive freshwater fish species can simultaneously bring benefits to the economy  (Gozlan 2008) , but the balance between impacts and benefits is still unresolved. The development of a comprehensive database of the positive and negative economic and ecological impacts of freshwater fish invasions could help clarify the benefits of certain species and consequently inform management decisions  (Vimercati et al.
2020). Regarding the lack of management data, the large number of articles on the management of freshwater fish species could provide the basis for a comprehensive database. Second, the further study of some aspects of freshwater fish invasions should be a priority. For example, several hypotheses regarding freshwater fish invasions are yet to be explored (see  Jeschke et al. 2018)  or fully understood (e. g. , the enemy release and biological resistance hypotheses). More generally, the spread and impact stages of the invasion process are less well studied than the other stages, and they could benefit from a better understanding if reliable data were collected (Figure  3 ). Third, the prediction of future invasion trends using predictive models and scenarios must consider all the drivers of invasion success.
ACKNOWLEDGMENTS We thank  Dan Simberloff  for his helpful comments, which improved the content of the review. We thank  Gordon Copp  for his comments on an early version of the paper. F. C. would like to acknowledge the  AXA Research Fund Chair of Invasion Biology of the University of Paris Saclay  and the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum call 2018. C. Bernery ,  C. Bellard , F. C. , and B. L. were funded by their salaries as French public servants.
Littorinid snails are present in most coastal areas globally, playing a significant role in the ecology of intertidal communities. Laevilitorina is a marine gastropod genus distributed exclusively in the Southern Hemisphere, with 21 species reported from South America, the sub-Antarctic islands, Antarctica, New Zealand, Australia and Tasmania. Here, an updated database of 21 species generated from a combination of sources is presented: 1) new field sampling data; 2) published records; 3) the Global Biodiversity Information Facility (GBIF) and The Atlas of Living Australia (ALA), to provide a comprehensive description of the known geographic distribution of the genus and detailed occurrences for each of the 21 species. The database includes 813 records (occurrences), 53 from field sampling, 174 from the literature, 128.
Introduction One of the most common challenges facing studies or the construction of inventories of biodiversity is the absence of detailed information on the distribution of taxa throughout the different geographical regions of the planet. Furthermore, species distribution data are usually scattered across different sources of information such as taxonomic reviews, species lists, reports and natural history collections  (Beck et al. 2013). Therefore, it is important to merge these different sources into robust and freely accessible biodiversity databases. The Global Biodiversity Information Facility (GBIF) project has enabled the creation of a platform where museums, herbaria and researchers can publish their databases and make them freely available for use  (Flemons et al. 2007 ). However, despite increasing the international effort devoted to the digitisation of specimen catalogues in museums and other repositories, even today only a small proportion of global records are estimated to have been made available online through the efforts of the GBIF and other platforms like the Ocean Biodiversity Information System (OBIS)  (Ariño 2010; Maturana et al.
The genus Laevilitorina Pfeffer, 1886 is characterised by a thick, generally smooth shell, a nonplanktotrophic protoconch and a generally paucispiral operculum  (Reid 1989; Warén and Hain 1996). At present, 21 species of Laevilitorina are taxonomically accepted (MolluscaBase 2022). The present study documents the state of knowledge of the genus and provides an updated database, using a combination of recent sampling data, published records available in the literature, and available information from GBIF and other repositories. The objectives of the study are: i) to report new records of Laevilitorina species present in Antarctic and sub-Antarctic environments and ii) to evaluate the distribution and richness of Laevilitorina species throughout the Southern Hemisphere, using an updated database.