dataesr/scientific-paragraphs-categorization huggingface dataset:
, n. d. ). However, we did find predatory alien birds to be associated with monetary costs. The cost calculations for our analysis are of combined management and damage costs, and not all costs associated with predatory alien bird species arise due to the management of their predation impacts; some are the result of the damage they cause to assets of value (e. g. agriculture). Predatory alien birds may therefore be associated with costs in part because they tend to possess characteristics that provide them with greater opportunity to cause impacts (and not only through predation of native species). Indeed, the alien bird species in our dataset with reported predation impacts tend to be diet and habitat generalists (average diet and habitat breadth score = 4 and 5, respectively) when compared to species that do not have reported predation impacts (2.
7 and 3. 2, respectively); they also tend to have larger alien ranges (average = 3,690,419 km 2 vs. 165,077 km 2 ). Nevertheless, predation by alien birds can be damaging to native species  (Evans, 2021; Evans et al. , 2016) , and due to the severity of these impacts, predatory alien birds may be targeted for management and thus be associated with monetary costs. For example, species in our dataset that have been managed due to their predation impacts include the African sacred ibis (Threskiornis aethiopicus), common myna (Acridotheres tristis), house crow (Corvus splendens) and weka (Gallirallus australis). Indeed, invasion biology research tends to focus on alien species with severe biodiversity impacts  (Pyšek et al. , 2008) , and there may therefore be more studies to identify predation impacts in comparison to other impact mechanisms, and hence more schemes to.
TA B L E 4 The relationships between the biodiversity impacts and monetary costs caused by alien bird species and 10 predictor variables. All parameters in this table derive from phylogenetic linear regression using the phylolm package in R (Tung  Ho & Ané, 2014)  to account for potential autocorrelation among species due to their phylogenetic relatedness. Results are the mean values for 100 phylogenies (lower and upper confidence limits (2. 5% and 97. 5%) are provided in parentheses). Significant relationships (p < 0. 05) are highlighted in bold. Total sample size = 40 species. Significance codes: '***'p < 0. 001, '**'p < 0. 01, '*'p < 0. 05. e. g. the common starling  (Fisher & Wiebe, 2006)  and the little owl (Athene noctua)  (Hayden, 2004).
Hypothesis However, because some alien bird species prey on other species to survive, predation impacts are likely to occur more frequently than impacts associated with some other mechanisms, such as hybridisation and transmission of diseases, and thus there may be more management schemes (with costs) to control and eradicate them. All parameters in this table derive from phylogenetic linear regression using the phylolm package in R (Tung  Ho & Ané, 2014)  to account for potential autocorrelation among species due to their phylogenetic relatedness. Total sample size = 40 species. Significance codes: '***'p < 0. 001, '**'p < 0. 01, '*'p < 0. 05. ~ = variable did not feature in model. Tompkins & Jakob-Hoff, 2011), and some predatory alien birds are relatively large species (though not always, see Figure  2 ) that prey on smaller bird species  (Evans, 2021) , and their size may make them easier to identify and control.
| Identifying factors associated with biodiversity impacts and monetary costs Our results suggest that certain factors afford alien birds greater opportunity to cause deleterious effects, whether they be on biodiversity or the economy, or both. Habitat and diet generalists are more likely to have impacts than specialist species restricted to a limited range of environments and food types. This may be because they have greater opportunity to cause impacts, as they are likely to interact with a broader range of native species and to come into contact with a wider range of assets of value to humans. Indeed, the species in our dataset with broad environmental tolerances also tend to be able to occupy urban environments, where they may cause biodiversity impacts in parks and gardens and affect assets of value such as buildings.
All but one of the eight species with the greatest habitat breadth in our dataset (occupying six or more broad habitat types) have severe biodiversity impacts and monetary costs >$0. Variables associated with the geographic distribution of alien species (alien residence time, and in particular, alien range size) were also predictors of monetary costs. This is likely to be because widespread species have a greater opportunity to cause deleterious effects on both biodiversity and the economy and in different ways at different locations (see Figure  1 ). For example, the common myna has one of the largest alien ranges in our dataset (>2,300,000 km 2 ; the median for all species in our dataset = 114,864 km 2 ). It has reported monetary costs on French Polynesia and Seychelles  (Evans et al. , n.
Indeed, over half of the 10 species in our dataset with the greatest habitat breadth and longest alien residence times have reported costs associated with their management. These costs are likely to increase as the distributional extent of an alien species' increases and it becomes more difficult to control or eradicate. The unsuccessful eradication of approximately 1 million house crows in Zanzibar (Tanzania), undertaken to address deleterious effects on both biodiversity and the economy (ZABISO, 2020), has so far cost approximately US$1. 5 million  (van Ham et al. , 2013) ; the successful eradication of a small population of house crows (<30) on Socotra Island (Yemen) cost US$20,500  (Suleiman & Taleb, 2010). Our results suggest that timely management interventions, sensu  (Hulme et al. , 2009) , to limit the distributional extent of alien bird species (particularly generalist species and those that prey on native species) may have important benefits for both biodiversity and the economy.
It may be too late to efficiently address the impacts of some widespread, generalist, predatory alien bird species such as the ring-necked parakeet (Psittacula krameri) in the UK, as these efforts are now likely to be costly  (Ahmed et al. , 2022). However, there may be opportunities to do so for recent alien bird incursions, and for those occurring in the future (alien species' numbers are predicted to rise considerably by 2050, including numbers of alien bird species)  (Seebens et al. , 2021). In the UK, for example, climate change may be assisting the establishment of the red-billed leiothrix (Leiothrix lutea)  (Broughton et al. , 2022). We identified a negative (though non-significant) trend between relative brain size and both severity of biodiversity impact and monetary cost. This is likely due to the presence of several parrot species in our dataset, which are large-brained and tend to have minor biodiversity impacts and low (or no) reported monetary costs.
That said, two parrot species in our dataset do have high reported monetary costs (the ring-necked parakeet and the monk parakeet [Myiopsitta monachus]), and the ring-necked parakeet also has 'more severe' biodiversity impacts; e. g. by preying on native bat species in Spain  (Hernández-Brito et al. , 2018). Notably, these are the two parrot species in our dataset with the largest alien ranges, longest alien residence times and greatest habitat breadth scores (all variables associated with severe biodiversity impacts and/or monetary costs).
| Comparing our results to past studies Although our dataset is relatively small (40 species; approximately onethird of the 116 species identified as having environmental impacts worldwide)  (Evans et al. , 2016) , the biodiversity impacts and monetary costs in our dataset are broadly distributed across the globe. In another global study  (Evans, Kumschick, et al. , 2018) , factors associated with the distributional extent of alien birds (alien range size and alien residence time) were found to be predictors of more severe biodiversity impacts. Our study is the first to test the influence of these factors on the socio-economic impacts (monetary costs) of alien birds, and we confirm that distributional extent not only drives more severe biodiversity impacts but also high monetary costs. Our results for generalism support the findings of previous studies, which have found habitat and diet breadth to be predictors of more severe biodiversity impacts at the global scale  (Evans, Kumschick, et al.
, 2014; Kumschick et al. , 2013) , but we did not identify any significant relationships with body mass at the global scale. For biodiversity impacts, this may be because analysis undertaken in the two regional studies did not include data on island impacts (as many islands occupied by alien birds are located outside of Europe). Biodiversity impacts caused by alien birds tend to be particularly severe on islands  (Evans, 2021; Evans et al. , 2021) , and some of these impacts (in our dataset) are caused by relatively small alien bird species such as the red-vented bulbul (Pycnonotus cafer). With regard to monetary costs, there are several relatively small alien bird species in our dataset that have costs by causing agricultural damage in low-income regions, such as the village weaver (Ploceus cucullatus) in the Dominican Republic; there are many low-income regions outside of Europe.
| Data deficiency and impact prediction Most alien bird species in our dataset do not have reported biodiversity impacts or monetary costs. It has been concluded that alien bird species with no reported biodiversity impacts that have large alien ranges and/or long alien residence times are likely to have minor biodiversity impacts because if these impacts were severe, they would have been noticed and reported  (Evans, Pigot, et al. , 2018). On this basis, we suggest that alien bird species with these traits that have no reported monetary costs are genuinely likely to have low monetary costs (those that can be monetised). However, as the impacts of alien species tend to be context dependent  (Pyšek et al. , 2020) , and human development influences the availability of impact data across different regions of the world  (Evans & Blackburn, 2020) , we cannot rule out that some alien bird species with no reported costs do actually have monetary costs particularly in less developed regions.
ACK N O WLE D G E M ENTS For their help in collecting ecological data for BirdBase we thank  Melissa Avalos ,  Alex Bennett ,  David Blount ,  Evan Buechley ,  Grant Doxey ,  Cara Drane ,  Shantell Garrett ,  Kayla Godfrey ,  Russelle Hansen ,  John Jackson ,  Kyle Kittelberger ,  Maddie Nelson ,  Burak Över ,  Jason Socci ,  Natasha Velasquez ,  Rory Weeks ,  Ashley Wiltsie  and  Sara Wiscombe. For collecting alien bird monetary cost data for the InvaCost database, we thank  Tasnime Adamjy ,  Celine Albert ,  Claire Assailly ,  Liliana Ballesteros-Mejia ,  Corey Bradshaw ,  Virginia G. Duboscq-Carra ,  Phillip Haubrock ,  Ivan Jaric ,  Melina Kourantidou ,  Eugenia Lopez-Lopez ,  Lise Nuninger ,  David Renault ,  Axel Rico-Sanchez ,  Nigel Taylor ,  Laura Verbrugge  and  Yuya Watari. For undertaking alien bird EICAT assessments, we thank  Tom Allmert.
Funding information Agence Nationale de la Recherche , Grant/Award Number:  ANR-18-EBI4-0004-07 ;  AlienScenario  project funded by  BiodivERsA  and  Belmont-Forum  call 2018 on biodiversity scenarios, Grant/Award Number:  I 4011-B32 ;  AXA Research Fund Chair of Invasion Biology ;  Barbara J. Watkins Environmental Studies Graduate  Fellowship;  BNP-Paribas Foundation   Climate Initiative , Grant/Award Number:  2014-00000004292 ;  DSI-NRF Centre of Excellence for Invasion Biology (CIB) ;  Hamit Batubay Özkan Conservation Ecology Graduate Fellowship ;  South African Department of Forestry, Fisheries and the Environment (DFFE) ;  Universiteit Stellenbosch; University of Utah Global Change and Sustainability Center.
FU  N D  I N G I  N FO R M ATI O  N This work was supported by the  French National Research Agency  [ ANR-18-EBI4-0004-07 ,  2018 ]; the  BNP-Paribas Foundation Climate Initiative  [ 2014-00000004292 , 2014]; the  AXA Research Fund Chair of   Invasion Biology  [2019]; the  AlienScenario  project funded by  BiodivERsA and Belmont-Forum  call  2018 on biodiversity scenarios  [ I 4011-B32 , 2018]; the  Hamit Batubay Özkan Conservation Ecology Graduate Fellowship ; the  Barbara J. Watkins Environmental Studies Graduate Fellowship ; the  University of Utah Global Change and Sustainability Center ; the  DSI-NRF Centre of Excellence for Invasion Biology (CIB) ;  Stellenbosch University ; and the  South African Department of Forestry, Fisheries and the Environment (DFFE).
F I G U R E 1The global distribution of the biodiversity impacts and monetary costs associated with 40 alien bird species. TA B L E 3The relationship between the total monetary costs of alien bird species and (1) the severity of their biodiversity impacts, (2) different types of biodiversity impact. All parameters in this table derive from phylogenetic linear regression using the phylolm package in R (Tung Ho & Ané, 2014)  to account for potential autocorrelation among species due to their phylogenetic relatedness. Results are the mean values for 100 phylogenies (lower and upper confidence limits (2. 5% and 97. 5%) are provided in parentheses). Total sample size = 40 species. Significance codes: '***'p < 0. 001 '**'p < 0. 01 '*'p < 0. 05.
Alien species with broad niches will have greater Habitat generalism V3: Habitat breadth opportunity to cause impacts. For example, those occupying a wide range of habitats are likely to interact with a more diverse range of severe impacts in Europe (Shirley & Kark, 2009) and Australia (Evans economy. Habitat generalist alien birds have been found to have more that some of their impacts will be damaging to biodiversity or the native species and assets of value to humans, increasing the chances Diet generalism conditions a species may tolerate) the breadth of environmental native range (as an indicator of The size of an alien bird species V4: Diet breadth V5: Native range size et al. , 2014), and diet generalist alien birds have more severe impacts in Europe (Evans et al. , 2014) H3: Diet preference (animal matter) V6: The proportion of a species' diet comprising animal matter Diet preference (seeds and fruit) V7: The proportion of a species' diet comprising seeds and fruit domly selected phylogenetic trees incorporating the 40 species in our dataset.
We used phylo. d (Fritz & Purvis, 2010) in the Caper package in R (Orme et al. , 2018) to calculate the D statistic (a mea- ). Species categorised as DD under sure of phylogenetic signal in a dataset) for each phylogenetic tree. EICAT and/or InvaCost were excluded. Due to small sample sizes for We identified phylogenetic signal in severity of impact (average certain EICAT impact categories (MR impacts, n = 3; MV impacts, D = 0. 4) with a low probability of D resulting from no phylogenetic n = 1), we converted EICAT data into a two-level variable: 'less severe' impacts (MC and MN impacts) , n = 23 species, and 'more severe' impacts (MO, MR and MV impacts) , n = 17 species. This divided impact severity such that 'less severe' impacts were those considered to be negligible (MC) or to affect the fitness of individuals of a native species (MN impacts), and 'more severe' impacts were those considered to be 'harmful' under EICAT because they cause declining populations of native species (MO impacts) and local or global species extinctions (MR and MV impacts, respectively).
Costs were analysed as a continuous variable (US$). As our dataset considers traits known to be influenced by phylogeny (e. g. body mass) and specific orders and families of alien birds are associated with specific types of impact (e. g. Anatidae with hybridisation), we expected our analysis to be influenced by phylogenetic autocorrelation. Following Evans et al. (2021) , we used Birdt ree. org (http://birdt ree. org/) to download 100 ranstructure (average p = 0. 03) or Brownian phylogenetic structure (average p = 0. 2). To address this, we analysed our dataset using phylogenetic linear regression (regression analysis incorporating phylogenetic methods) (Revell, 2010)   (the phylolm package in R) (Tung Ho & Ané, 2014)   to account for potential phylogenetic relatedness among species. TA B L E 2 However, they have reported biodiversity impacts on these islands (e.
The complete measure change algorithm yields the following system, (Complete)            ṗ1 t = K N -1 N cos( Xt -X 1 t ) -cos(X 1 t ) p 1 t -K N cos(X 1 t -Xt )p 2 t , p 1 T = 2b , ṗ2 t = -K N -1 N cos( Xt -X 1 t )p 1 t + K 1 N cos(X 1 t -Xt ) + cos( Xt ) p 2 t , p 2 T = 0 , Ẋ1 t = K N -1 N sin( Xt -X 1 t ) -sin(X 1 t ) + 1 2 σ 2 p 1 t , X 1 0 = x 0 , Ẋt = K 1 N sin(X 1 t -Xt ) -sin( Xt ) + 1 2(N -1) σ 2 p 2 t , X0 = x 0 , To show the numerical advantages one can achieve by using importance sampling we consider how the time taken and the estimate given by the algorithms change with the number of particles N. For this example we use, T = 1, X0 = 0, K = 1, σ = 0. 3, a = 0. 5 and b = 10. For the numerics we use an Euler scheme with step size of ∆t = 0. 02. The systems of equations are solved using MATLAB's bvp4c function.
*  G. dos Reis  acknowledges support from the  Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology)  through the project  UID/MAT/00297/2013  ( Centro de Matemática e Aplicações CMA/FCT/UNL). † G. Smith  was supported by The  Maxwell Institute Graduate School in Analysis  and its Applications, a  Centre for Doctoral Training  funded by the  UK Engineering and Physical Sciences Research Council  (grant  EP/L016508/01 ), the  Scottish Funding Council , the  University of Edinburgh  and  Heriot-Watt University. ‡ The research of  Peter Tankov  was supported by the  FIME Research Initiative  www. fime-lab. org.
This paper examines the cost of explainability in machine learning models for credit scoring. The analysis is conducted under the constraint of meeting the regulatory requirements of the European Central Bank (ECB), using a real-life dataset of over 50,000 credit exposures. We compare the statistical and financial performances of black-box models, such as XGBoost and neural networks, with inherently explainable models like logistic regression and GAMs. Notably, statistical performance does not necessarily correlate with financial performance. Our results reveal a difference of 15 to 20 basis points in annual return on investment between the best performing black-box model and the best performing inherently explainable model, as cost of explainability. We also find that the cost of explainability increases together with the risk appetite.
Original Contribution Our paper makes a threefold contribution. First, we compare the standard AI models, both the explainable and the black-box ones, with two newly developed explainable models that use black-box techniques to produce a GAM, which aim at balancing performance and interpretability. Second, we evaluate the models for their regulatory compliance and financial performance, considering the lender's credit risk appetite, funding costs, predicted PD, and LGD. Third, we introduce the concept of "cost of explainability" to measure the financial cost of using inherently explainable models. Therefore, we compare their achieved economic performance with that of the most performant black-box models. The rest of the paper is organized as follows: Sect. 2 describes the dataset and data management.
Description Our study evaluates various credit scoring models using a dataset supplied by the world leader in trade credit insurance, covering the period from 2019 to 2022. The dataset consists of an anonymized limited and representative set of one-year credit exposures on a group of European borrowers reporting legal and financial information, with total assets above 1 million euros. In total, there are 55 explanatory variables, including 14 balance sheet items, 4 income statement figures, 9 financial ratios, and 9 legal and company descriptive features, plus one year lagged financial information. For confidentiality reasons, we cannot disclose details on the explanatory variables. We use a training set of 2019 data (76089 rows), and test sets from 2020 to 2022, with 44151, 61406, and 59074 rows, respectively.
Data Management Missing Data. The dataset we use for our analysis contains missing values. While some machine learning algorithms can handle missing data, most cannot. Therefore, we test several imputation techniques on the training data to handle the missing values. For univariate imputation, we replace missing values with the median values from the training set. For multivariate imputation, we use KNN, as introduced by  [38] , which selects the k-nearest neighbors' to determine the imputation value. We also employ a Bayesian Ridge algorithm with a round-robin iterative process, as described in  [39]. We analyze the data drift induced by the imputations in the training data using four different tests: the Population Stability Index (PSI), Kolmogorov-Smirnov (KS), Kullback-Leibler divergence (KL), and Jensen-Shannon divergence (JS) tests.
Reproducibility and Replicability. To ensure the reproducibility and replicability of our tests, we take several measures. First, we fix the seed for Python, Numpy, and Pytorch. Second, we enforce Pytorch to work with deterministic CUDNN. Furthermore, we perform both training and testing on two different computers and confirm that we obtain identical results. To verify the robustness of each model, we conduct additional training and testing using marginally modified hyper-parameters.
Discriminatory Power: We use the AUROC as a metric to evaluate the discriminatory power of the models. The AUROC measures the ability of the model to distinguish between good and bad credit risks by calculating the area under the receiver operating characteristic (ROC) curve. To ensure that the models accurately separate risky borrowers from less risky ones, we evaluate their rank-ordering performance using the Mann-Whitney U statistic. A higher Mann-Whitney U statistic indicates better rank-ordering performance and, therefore, a more effective model. Unlike the parametric AUROC, which assumes that the predicted probabilities follow a certain distribution, the Mann-Whitney U statistic does not require any assumptions about the shape of the distribution. This makes it a more robust measure for assessing model performance on imbalanced datasets.
We acknowledge that with this dataset, we have one-year credit exposures, so the one-year PD is equal to the lifetime PD. For a similar analysis including credits with longer maturities, we should consider the difference between one-year PDs and lifetime PDs. We can compute the percentage of accepted loans and the return on invested amount for each model. We perform the base case with rfr = 3. 25%, ftp = 0. 75%, cm = 0. 50%, and the risk appetite threshold set at grade 6 on a scale from 0 to 9, corresponding to the breakpoint of 4. 33% in the reference master scale. We then test the sensitivity of the returns to a change in the risk appetite threshold. 6 The results of our empirical analysis indicate that three models are unable to produce a master scale with sufficient grades at a minimum distance of 0.