dataesr/scientific-paragraphs-categorization huggingface dataset:
Their decision may vary with context since, on the one hand, data inferred by this method may invalidate evolutionary models, but on the other hand they can refine models of community assembly wherein the inclusion of like-for-like proxies is better than deleting species F I G U R E 3 Morphological trait sampling for all bird families. AVONET contains 718,662 individual trait measurements, all of which are used to calculate species averages. However, sampling per species varies across families depending on taxonomy. Upper phylogram shows sampling under BirdLife International (11,009 species in 243 families). Families where sampling completeness is below 75% indicated by lighter shading. Most families with lower sampling are species poor (numbers in black circles show species richness). Lower panels show that sampling improves under more conservative taxonomic treatments of eBird (10,661 species in 249 families) and BirdTree (9993 species in 194 families).
Geographical sampling The 75,843 specimens contributing measurements to this dataset are held in 78 institutional or private collections in 31 countries worldwide (Figure  4 ). There is a strong bias to the Global North, particularly Europe and North America. Further coordinated sampling of museum collections is most urgently required in Africa and across much of Asia. Pooling across both museum and field data, we were able to assign 77,245 (85. 8%) of 90,020 sampled individuals to a specific source country, leaving 12,775 (14. 2%) unassigned. Mapping these countries of origin, and overlaying with species geographical ranges, revealed that our morphological traits have been sampled from populations in 206 administrative units, including 181 sovereign countries. Given the preponderance in our dataset of specimens from the Natural History Museum, London, it is no surprise to see a relatively high sampling in regions associated with the former British empire, including South and East Africa, India, Malaysia, Australia and New Zealand (Figure  5b, c ).
Intraspecific trait variation Previous analyses based on earlier versions of this dataset indicate that most variance in trait values occurs among (98. 25%) rather than within (1. 75%) species  (Pigot et al. , 2020) , suggesting that species are a valid sampling unit for bird traits at large taxonomic scales. This contrasts with the situation in plant traits, which typically vary within species according to local conditions (light, soil chemistry, hydrology, etc. ). Since the publication of  Pigot et al. (2020)  we have added multiple parallel datasets, increasing intraspecific sampling by 71%, rising from around 5. 0 individuals per species to 9. 0 individuals per species. In tandem, coverage by sex has increased to an average of 3. 0 females, 4. 6 males and 1. 4 unsexed individuals per species (BirdTree dataset; Table  S1 ).
Macroecological patterns of trait variation We focus here on presenting an overview of the AVONET data rather than testing particular hypotheses. To illustrate potential uses of the data, we plotted macroscale geographical and ecological patterns for three key traits-HWI, tarsus length and beak length (Figure  6 ). At a global scale, spatial patterns show that HWI increases towards the poles, indicating strong variation in dispersal ability across latitudes and in relation to particular lifestyles. Relative tarsus length increases in less vegetated biomes (i. e. steppes, grasslands and deserts), reflecting adaptation to a more terrestrial lifestyle. Relative beak length shows a mirror image to this pattern, with increases in well-vegetated regions, including tropical rainforests. Definitions for relative beak and tarsus length are given in Figure  6.
DI SC US SION Our understanding of biodiversity is ultimately limited by the availability of data in a few critical areas, including species geographical distributions (the so-called 'Wallacean shortfall') and traits (the 'Raunkiaeran shortfall';  Hortal et al. , 2015). AVONET goes a long way towards addressing these shortfalls by summarising a complete set of morphological trait measurements, along with discrete ecological and geographical variables, for all extant species of birds. This resource provides a foundation for theoretical tests and technical innovations in ecology, evolution and conservation biology, yet also highlights the need for further sampling at the intersection of Wallacean and Raunkiaeran shortfalls, where many gaps remain in the intraspecific and geographical sampling of bird traits.
F I G U R E 5 AVONET presents raw morphological data for 90,020 individual birds at an average of 8. 1-9. 0 individuals per species (varying by taxonomy), providing a foundation for a new generation of studies investigating or accounting for intraspecific variance. This figure illustrates how variance is partitioned for a key morphological trait (beak length). Left-hand panels show that most variance is explained at higher taxonomic levels (orders, family and species), whereas intraspecific (individual) variation is contrastingly low, supporting the use of species averages in comparative studies. Curves are normal distributions based on SD; percentages (%) show proportion of variance at each level. Right-hand panels show beak length variance within families and within species (restricting to families with >5 species and species with >5 individuals measured; note different axis scales in upper and lower panel).
Sequential ranks show a 'hockey-stick' distribution with examples of the most extreme outlier family (Scolopacidae) illustrated. Extreme within-species values for beak variance may reflect polymorphism or, in some cases, measurement error Our analyses show that morphological trait data in AVONET were sampled from bird populations throughout the world, with intensive sampling in many tropical regions. However, we also show that access to specimens underlying the dataset was distributed very unevenly, with most reference material stored in the collections of North American and European museums (Figure  5a ). We hope that the publication of AVONET helps to remove these longstanding biases in access to museum collections by mobilising specimen data for global use. Morphological traits of animals may fail to fulfil their promise of delivering ecological insights and theoretical advances if their connection to function varies idiosyncratically across evolutionary lineages or if they are uninformative about niche-based processes and interactions  (Didham et al.
, 2016). These concerns are particularly relevant for global datasets wherein species coverage is so high that the set of traits assembled is relatively limited. Fortunately, these potential issues do not appear to undermine the utility of avian morphological traits given they predict trophic niches with remarkable accuracy  (Pigot et al. , 2020) , including species interactions across trophic levels  (Dehling et al. , 2016). Moreover, we show that the same traits are also strongly indicative of habitat biomes and primary lifestyle. These results suggest that avian morphology is not only ecologically informative, but that the relationships between form and function are sufficiently general to develop the kind of predictive framework envisaged by  Winemiller et al. (2015). Furthermore, we show substantial trait variation within taxonomic groups, lifestyles and diet categories, highlighting the importance of quantitative trait data as a basis for moving beyond the subjective and ultimately coarse categorical data used in most previous analyses of vertebrate functional traits  (Kohli & Jarzyna, 2021).
The earliest phase of AVONET development involved different research groups independently collecting smaller subsets of trait data to explore a range of topics F I G U R E 6 Species-level variation in avian functional traits in relation to geography and lifestyle. Hand-wing index (wing elongation) peaks towards high latitudes (a), and in species with aquatic and aerial lifestyles (b); relative tarsus length peaks at mid-latitudes and non-forest regions (c), and in species with terrestrial lifestyles (d); relative beak length peaks in the tropics, including rainforests (e), and in nectar feeders and aquatic predators (f). For maps, median trait values were calculated for 18,709 grid-cell assemblages worldwide. Darker colours indicate larger trait values. Assemblages were delimited by extracting species native resident or breeding distributions (n = 10,964 species for which both trait and geographical range data are available) onto an equal area grid with a cell resolution of ~100 km (Behrmann projection).
Relative beak and tarsus length are the residuals of a linear regression of log-transformed tarsus and beak length (mm) against log-transformed body mass (grams). Species in (b,d) are classified according to primary lifestyle (predominant locomotory niche; insessorial = perching lifestyle). Species in (f) are classified according to primary diet following  Pigot et al. (2020). Sample sizes (b,d,f) are numbers of species in each category in ecology and evolutionary biology, including adaptive radiation  (Claramunt, 2010) , character displacement  (Tobias et al. , 2014) , community assembly  (Trisos et al. , 2014)  and the predictive properties of traits in relation to ecological networks  (Schleuning et al. , 2015). The maturation and integration of these trait datasets into AVONET allows hypotheses to be tested at an ever-larger scale.
The next step is to improve intraspecific sampling and geographical coverage for all species-common and rare-to provide a better description of individual trait variation across space and time  (Des Roches et al. , 2018). Ideally, this will involve continued sampling of historical museum collections, as well as wild individuals mist-netted and released by field projects, to provide the most complete time series and widest spatial sampling. To these ends, we have included missing species to highlight sampling coldspots in Supplementary Dataset 1, and supplied our trait sampling protocol (Supplementary Materials) to improve the standardisation of morphological trait data for incorporation into AVONET.
CONC LUSIONS We present a complete description of morphological and ecological trait diversity for all birds (Aves), the largest class of tetrapod vertebrates. The achievement of total species coverage at this scale sets a new standard for morphological trait data. All specimenlevel data and metadata are included. Our results confirm that avian traits have distinct associations with diet, environment and lifestyle, offering a framework for testing ecological theory, identifying underlying mechanisms and predicting the response of biodiversity to global change. Although we have focused exclusively on birds, we hope this venture will inspire similar efforts across other animal systems, opening up the possibility of future integration across vertebrate databases.
AC K NOW L E DGE M E N T S We thank the Natural History Museum, Tring, UK, the American Museum of Natural History, USA, and 76 other research collections for providing access to specimens. Illustrations are reproduced with permission of Lynx Edicions/Cornell Lab of Ornithology. Financial support was received from numerous sources, with extensive support from Natural Environment Research Council grants NE/I028068/1, NE/P004512/1 and UKRI Global Challenges Research Fund grant ES/ P011306/1 (JAT). All major sources of funding are listed in Supplementary Material, along with a complete list of institutions and individuals that contributed to data collection, logistics and specimen access.
AU T HOR S H I P JAT initiated and developed the core dataset and conceived the publication. JAT, MS, GHT and SAF worked on the conceptual framework for the paper. CS coordinated the initial phase of data collection and measured over 10000 specimens. MHCN, NA, HEAM, PAW, SEIJ, CV, AGP, NMM, FMC, VLS, SC, BD, BGF and TPB also collected substantial trait datasets (>1500 specimens measured). LNN, KBG, CR, GHT, SAF and MS coordinated independent trait datasets and helped with merging data. Additional authors contributed smaller trait datasets. AJMD, TLW, RAB, JY, PAW and FS helped with data management and integration of datasets. ALP, AJMD, JY, FS, SAF and JAT conducted analyses and produced figures. JAT wrote the first version of the manuscript with input from MS and ALP. All authors contributed critically to subsequent drafts and gave final permission for publication.
MCDA The Visual PROMETHEE software (VP; ULB, Brussels, Belgium) was used to perform MCDA and obtain comparative rankings of a set of alternatives. Overall numerical scores assigned by the participants were entered for each criterion on VP. Following this, results were visualized using a visual model performed by the GAIA (Graphical Analysis for Interactive Aid) tool provided in VP. Group rankings of strategies yielding the best to worst alternatives were performed using the individual stakeholder's values expressed via criteria weighing.
However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested. (1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model. (2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison. (3) Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units. (4) The training dataset is composed of known economic mineralisation sites (deposits) as 'positive' examples, and exploration drilling data providing 'negative' sampling locations.
Acknowledgment of this has encouraged development of rapid and repeatable geological map deconstruction workflows offered by 'map2model' and 'map2loop'  (Jessell et al. , 2021) , a key open-source module in the  'Loop' platform (loop3d. github. io; Ailleres et al. , 2019). Initially designed for extraction of geological inputs for 3D modelling, the same inputs can be easily adjusted for MPM as demonstrated in this contribution. Training-point sets provide so-called 'data-driven' methods (weights-of-evidence, logistic regression, random forests and neural networks) with locations from which features are sampled to generate a feature set that characterises the mineral system (e. g. Lindsay et al. , 2014; Ford et al. , 2015; Kreuzer et al. , 2015). The training point set is typically populated with mineral deposit locations, usually operating or past mines.
Additional datasets 3. 3. 1. 2D data derived from map2loop analysis 'map2loop' and 'map2model' are python libraries that automatically combine information contained in geological maps with conceptual information to provide geometric, positional and topological constraints for three-dimensional geological modelling    2022 ) 101435  (Grose et al. , 2021; Jessell et al. , 2021). While not intended for mineral prospectivity modelling, map2loop/map2model (hereafter known as map2loop) produces similarly relevant constraints to predict mineralisation. More obvious features traditionally used in mineral prospectivity analyses are produced: geological maps; fault and fold trace representations; digital elevation models. Potentially useful features are automatically produced by map2loop: stratigraphic thickness estimates, fault displacements and dip angle estimates.
Mineralisation and barren drillholes Training points representing economic mineralisation are taken from the GSWA mineral deposit database 'MINEDEX' (Geological Survey of Western Australia, 2020a) which contains the location and description of mineral deposits in Western Australia. Martite-goethite (MÀG) and martite-microplaty (MPL) deposits were included in the training set, and channel-iron deposits (CID) and points not listing Fe as the primary commodity were removed. This sub-set of MINEDEX represents positive points for economic Fe ore mineralisation. We augment the training point set with generation of eight additional points located around each deposit location (Fig. 4 ) arranged radially at each of the ordinal (100 m distance) and interordinal compass directions. Adding these points increases the training set size from 82 to 738 points and addresses a commonly known limitation of supervised classification methods when applied to spatial geoscience questions  (Karpatne et al.
, 2019). The practice of training set augmentation performed in this manner is common, and is justified as the additional sites are based on the location of known and robust examples  (Sun et al. , 2011; Prado et al. , 2020). A richer representation of potential heterogeneity is also achieved through adding these additional sites, as the sampling area around sites of mineralisation is increased to an area more representative of the spatial extent of these sites in the real world. Fe ore mines are often larger than the 100 m 2 area that one cell occupies. Negative training data are represented by the collar location of drilling performed for the purposes of Fe ore exploration (Geological Survey of Western Australia, 2020b). These locations are assumed appropriate as negative indicators of economic mineralisation for the following reasons.
They are considered as geologically plausible locations for Fe mineralisation, however we assume do not return consistently high Fe assay values indicating the necessary tonnes of mineralisation or the required geological and economic characteristics to justify construction of a mine. A 2 km exclusion zone around the original deposit sites was implemented to avoid including drill locations that may represent positive evidence. Drill locations within the 2 km exclusion zone around a mine may have led to discovery of economic mineralisation and thus eventual mine development so were not included in the negative evidence set. Over 10,000 points are available as negative evidence sites from the GSWA drilling dataset. We select 1740 sites pseudo-randomly to provide a balanced training set. While forest-based models are less susceptible to undersampling minority classes or validation errors due to imbalanced training sets  (Carranza and Laborte, 2015; Hariharan et al.
Remote-sensing and geophysics Geophysical datasets (reduction-to-pole magnetics, sphericalcap Bouguer gravity, K-band radiometrics) are input to the forestbased classification model in grid format in the format as supplied by the GSWA  (Brett, 2020a (Brett, , 2020b (Brett, , 2021)). The magnetic grid has 40 m cell size. The K-band radiometric grid has 80 m cell size. The gravity grid has 400 m cell size. ASTER datasets were provided by the CSIRO Geoscience products as supplied  (Cudahy, 2012)  with a 90 m cell size. The SRTM DEM was obtained from Geoscience Australia at https://services. ga. gov. au/site_9/services/DEM_SRTM_1Sec ond_over_Bathymetry_Topography/MapServer/WCSServer?reques t = GetCapabilities&service = WCS (last access: 02 February 2020) and has 90 m cell size. No additional processing was performed on these datasets (Fig.
5a, b ). Radiometric K-band and ASTER Si content features represent the near-surface rocks (Fig. 6 ). These may be outcrop, subcrop, or transported cover with the proportion of each varying across the study area. The radiometric and ASTER datasets are included because they may provide unique and useful information not otherwise available from other geophysical features that respond to deeper rocks. The macroscopic radiometric patterns are constrained by the bedrock geological boundaries, with some smaller scale heterogeneity between boundaries. The ASTER Si content is better constrained by the regolith, or surface, geological formation boundaries. Our interpretation is that the radiometrics does represent the bedrock, either as outcrop, or in-situ subcrop while the ASTER Si content represents the surface geology, including transported regolith, sands and very recent formations.
Fault and fold buffers Faults and folds are represented as vector polylines in geological map datasets. Points representing mineral deposits are rarely sited exactly on the fault or fold polylines due to issues of scale and the use of 0D points and 1D lines to represent 3D objects like faults and folds. Thus, sampling for faults using points almost never forms a useful association for the modelling algorithm. Common assumptions that deformation or hydrothermal process related to mineralisation occur in regions around, and not always on, faults and folds justifies using buffers and distance-based representations (e. g. Bonham-Carter, 1994; Cheng and Agterberg, 1999) , in addition to uncertainties around exact position of the fault  (Occhipinti et al. , 2016). Buffers that form an area of influence around faults and fold are considered an appropriate representation.
Forest-based classification Forest-based classification trains a model based on known values provided from a training set. The model is then used to predict unknown values using the same features in a prediction dataset. The 'Forest-based Classification and Regression' tool available in ArcGIS Pro v. 2. 9 (documentation: pro. arcgis. com/en/pro-app/latest/tool-reference/spatial-statistics/forestbasedclassificationregression. htm) is an adaptation of Leo Breiman's random forest algorithm and was used for the modelling  (Breiman, 1996 (Breiman, , 2001)). The tool creates an ensemble, or 'forest', of decision trees for prediction. A prediction is made by each tree, and a voting scheme is used to produce the final predictions from the ensemble. The Gini coefficient is used to determine the quality of a split by measuring the heterogeneity of classes in the child nodes with respect to the parent node  (Breiman et al.
, 1984). Classic decision-trees often use the first feature to split, so feature order influences the decision boundary. Using an ensemble helps to reduce overfitting to the training set as does using a random subset of features in each decision tree to avoid bias associated with feature ordering. Forest-based classification has a large uptake in scientific research due to a record of producing reliable predictions from natural science datasets. Araki et al. (2018)   Sensitivity analysis was performed and the final hyperparameters for the classification are summarised in Table  2. Each model was generated using these hyperparameters and checked against various useful metrics (section 4. 1) available to forest-based classification. These metrics assist model 'explainability' (i. e. understanding which parameters have undue influence over model generation) and are another good reason to use forest-based methods to generate natural science models.
Out-of-bag errors Out-of-bag prediction errors indicate the accuracy of a forestbased classification model. This metric estimates how well each tree can predict data points used in the training dataset but not used in the decision trees making up the forest. In other words, OOB tells us how well each tree predicts excluded data points. Fig. 9a  displays the OOB error as a percentage of incorrect classifications for each training point category (economic and noneconomic mineralisation), that is how well the model can predict training points that the classifier has not seen. Fig. 9  displays the mean-squared error (MSE) for each model which indicates the proportion of incorrect OOB classifications over all categories. The overall results indicate each of the models make robust predictions. That the MSE or OOB errors are not too close to zero also demonstrates the model is not overfitting  (Cutler et al.