dataesr/scientific-paragraphs-categorization huggingface dataset:
Moreover, this metric can be computed either for the full PDF or focusing only on segments of the PDF. Following  Soares and Cardoso (2018)  the added value for the extreme section of maximum (minimum) air temperature was also analysed considering only the values above (below) the observational 90th (10th) percentile. For a fair comparison, simulations and observations must be at the same resolution when comparing two gridded datasets Hence, following the EURO-CORDEX guidelines, the FPS-Convection models are conservatively remapped  (Schulzweida et al. 2006 ) to the 0. 1° (~ 11 km) regular grid of the observations so that degradation of the high resolution is minimized. For the same reason, the E-OBS is also conservatively remapped into the ERA-Interim 0. 7° (~ 75 km) resolution. In this approach, the interpolation to lower-resolution grids may degrade the PDF, particularly in the tails  (Prein and Gobiet 2017).
Another option was to interpolate all simulations into the 0. 1° (~ 11 km) regular grid of the observations. This form allows for a comparison with all grids at the same resolution however, unrealistic values may be generated through the interpolation  (Ciarlo et al. 2020). Concerning the EMO-5 dataset, all CPRCMs are interpolated into the 5 km grid resolution of the observations. For (1) S = n ∑ i=1 min Z m i , Z obs i (2) DAV = 100 × S hr -S lr S lr the lower resolution RCMs and ERA-Interim, the EMO-5 dataset is interpolated into each model simulation native grid resolution. Whenever an interpolation is carried out, an adiabatic temperature correction was performed to ensure that all comparisons are computed at the same height, i. e. , before the model interpolation, temperatures are adjusted to sea level with a constant lapse rate of 6.
Maximum temperatures full PDFs This section presents the results obtained by applying the DAV metric to the daily maximum and minimum temperatures output for the FPS-Convection models, by considering either the E-OBS regular gridded dataset, the EMO-5 highresolution gridded dataset or the ECA&D station dataset as ground truth. For all datasets involved, the full PDFs shown in the supplementary material Figures S1 and S2 are considered to build the DAV shown in Figs. 2  and 4. Figure  2a  displays the scores attained by the ERA-Interim against all three datasets. The higher the score, the more difficult would be for the downscaled products to reveal added value as the limit is a score of 1. Thus, small gains, even in the situation where ERA-Interim shows scores close to the unit, are relevant.
The ERA-Interim results for all seasons from the three observational datasets are close, apart from summer and autumn. Under these circumstances, similar DAV values among all three observational datasets are expected. Moreover, with the higher score values for the YEAR and SON, near neutral or negative DAV from the downscaled products can be anticipated. Figure  2b  displays the DAV for the annual scale. Indeed, from the score obtained by the driving simulation, the RCMs and CPRCMs reveal neutral or even a small detrimental effect, with the values bounded between -5 and 5%. The results do not deviate significantly among datasets. Only the DAV from the ECAD for the RCMs reveals a slightly different behaviour with negative values, with the minimum at approximately -7%. For the CPRCMs, the DAV reveal a lower variability across all simulations with a median close to 0%.
For winter and spring, highlight the high RCM variability with ECA&D as a reference, which is greatly reduced for the CPRCMs. For summer and with the E-OBS and EMO-5 as references, most RCMs and CPRCMs reveal high DAV values, with the maximum above 10% in some cases. For this season, the CPRMs reveal a slightly detrimental effect relative to their lower resolution RCMs counterparts, as seen with the median below 0%. These higher values are related mostly to the poorer performance of ERA-Interim in comparison with the other seasons. For the ECA&D as a reference, the score for the ERA-Interim is higher in comparison to the E-OBS or EMO-5. Nevertheless, the value for summer is the minimum obtained among all seasons and datasets. For SON, the DAV pattern attained by the downscaling products from ERA-Interim is similar to summer, albeit with a smaller variability and values closer to 0%.
In some sense, the near absence of added value obtained in almost all situations is not surprising since ERA-Interim assimilates the observational weather data and RCMs and CPRCMs do not. Figure  S3  shows these results, but for the individual models. The similarity of the results obtained between E-OBS and EMO-5 is relevant and shows that even if a higher resolution dataset is considered, the DAV values would not change significantly. As for the ECA&D station network, the results reveal an increased variability across all models. Perhaps this increase might be related to the scarcity of stations outside of Germany or since for this case an interpolation is not required, as only the nearest neighbour grid to the station point is considered. An advantage of not using an interpolation step is related to the potential of not adding uncertainties to the final result.
Thus, a value of 1 implies no differences between the model and ERA-Interim statistics and potentially results in a neutral DAV. Values below 1 imply a better performance of the models, for the mean bias and root mean square error and a positive DAV can be expected, while the opposite occurs for values above 1. For the standard deviation, a value of 1 implies no changes from ERA-Interim to the models. Although the very high-resolution CPRCMs inherit the biases from the highresolution RCMs, the first are still able to improve, getting closer and sometimes surpassing the driving ERA-Interim performances. A spatial representation of the added value for the maximum temperature full PDF is shown in Fig. 3 , with the regular gridded E-OBS observation dataset as a reference. These figures are built by considering each pixel of the native 0.
The supplementary Figures S7 to S9 display the DAV result with the E-OBS, EMO-5 and ECA&D datasets as references, respectively. The results among the three datasets are very similar even for the ECA&D stations, particularly in Germany, where the station density is higher. Yet, among the results for all observational datasets, the Mediterranean coast displays added value, contrasting with the near-neutral or even negative values in the interior. Furthermore, this spatial assessment also allows one to evaluate the gains at different height levels. Since the downscaled products are evaluated at a higher resolution in comparison to ERA-Interim, each grid cell would be associated with a certain height range. Overall, for maximum temperature the FPS-Convection simulations reveal gains near sea level, contrasting with some difficulties over the complex terrain of the ALPs and near neutral values over the rest of the domain.
With the EMO-5 as a baseline, the results are similar, albeit more than 50% of the RCMs reveal added value. For the ECA&D all FPS-Convection simulations display losses. However, in this case, the improvement for the CPRCMs is higher in comparison with the other datasets reaching + 15%. For spring, the difference among datasets is similar, but with lower DAV for E-OBS and EMO-5. As for summer, the improvement from the CPRCMs in comparison to RCMs is non-existent for the E-OBS while for EMO-5 and ECA&D, approximately half the model's loose value at the higher kilometre-scale resolutions. For autumn, the pattern of DAV values is similar, albeit with a smaller variability among all the models. The individual model results are displayed in Figure  S10. A rationale for this general decrease of value from the FPS-Convection models is mostly due to two factors which complement each other.
An even finer resolution is required for the correct simulation of the latter phenomena. Overall, the higher-resolution RCMs can better depict the mountain ridges and valleys, while at the same time they reveal an inability to adequately represent snow cover, depth and melt. These issues impact the snow-albedo temperature feedback through the balance between latent and sensible heat fluxes leading to a misrepresentation of temperature around 0 °C  (García-Díez et al. 2015; Minder et al. 2016; Terzago et al. 2017). This causes an underestimation of snow depth at lower altitudes and an overestimation at higher elevations. Given the topographic complexity of the Alps region, these issues play an important role, especially at higher resolutions and altitudes. Figure  S11  to S13 display the mean bias, standard deviation and RMSE respectively, from all simulations against all considered observational datasets, complementing the PDFs shown in Figure  S2.
Indeed, the Era-Interim reanalysis reveals a remarkably good performance with smaller biases and a close variability to the E-OBS dataset. Thus, it is expected an added difficulty for the downscaling products to reveal a better performance than the driving reanalysis. In comparison to maximum temperature (Figure  S3 ), the models reveal a systematic poorer performance than Era-Interim hence the overall loss of value. Nevertheless, the range of RMSE differences is similar to the maximum temperature, particularly for E-OBS and EMO-5. However, the standard deviation reveals a distinct perspective. Indeed, for minimum temperature (Figure  S12 ) the variability of the FPS-Convection models relative to observations is much higher. The lower DAV percentages for minimum temperature in comparison to the maximum temperature were also found in  Careto et al.
Maximum and minimum temperature tails (or extremes) Figures 6 and 7 display the added values regarding the extreme tails of maximum and minimum temperature, respectively. In this case, the PDFs are built considering only the maximum (minimum) temperatures above (below) the 90th (10th) observational percentiles given in Figure  S1  (S2). The versatility of DAVs, being able to be applied to PDF sections, allows for an assessment of the added value related to a segment, which may not stand out when considering the entire PDF. The maximum temperature extremes added value, shown in Fig. 6  where the E-OBS dataset is the baseline, displays a different pattern when compared to Fig. 2. First the score attained by the ERA-Interim is lower when compared with Fig. 2a , particularly for the winter season.
At the annual scale (Fig. 6b ), the DAVs from at least half the RCMs are negative for all observational datasets, with a significant difference between the minimum value and the 25th percentile, displaying -25% and -5% respectively. In this case, the CPRCMs are not able to correct those losses, and most models reveal a loss of value. For winter, the panorama differs. The lower ERA-Interim score provides room for improvements from the FPS-Convection models. Indeed, all simulations evaluated against E-OBS, and EMO-5 display higher gains, surpassing + 20% and more than half the RCMs display DAV above + 15%. For the ECA&D, the results are more neutral for the RCMs with the kilometre-scale showing improved performance. For spring, the results obtained are In each boxplot, the low whisker denotes the minimum DAV, while the high whisker represents the maximum.
CPRCMvERA measures the DAV between the CPRCM simulations against the ERA-Interim reanalysis. CPRC-MvRCM measures the DAV between the CPRCM and the RCM simulations simulations tend to lose value relative to the respective forcing RCMs. The cold extremes DAV are shown in Fig. 7. Among all cases previously assessed (Figs. 2a, 3a and 6a), the score for the ERA-Interim from all three datasets is higher. The exception is for summer and with the ECA&D as a reference. Also, as expected from Figs. 7a and S2 a general loss of value occurs for all simulations at the annual scale (Fig. 7b ). Still, the CPRCM are able to add value, partly correcting the results from the RCMs. Yet this improvement is not sufficient as almost all FPS-Convection simulations display detrimental effects. At the seasonal scale, the results for winter, spring and autumn are similar to the annual scale.
Nevertheless, the improvement found for the FPS-Convection models near the coast surpasses the DAV obtained for the full PDF (Fig. 3 ). All gains in the tail distribution of maximum temperature are especially relevant, due to a possible better characterization of extremes of temperature and the related heat waves. As before, Figures S19, S20 and S21 display the spatial DAV against all three respective observational datasets for the individual models. Figure  9  displays the spatialization of the DAV results for the cold extremes, i. e. , only for values below the observational 10th percentile. While for the whole PDF, the DAV are mostly negative, when assessing the results at a more local scale, the situation differs. Overall, more than 50% of the RCMs reveal added value for most of the domain.
This is supported by the slightly different results of the models obtained with the ECA&D stations as a reference, namely in the extremes. It is also worth noting that the E-OBS gridded dataset is mostly based on the ECA&D stations which are sparsely distributed across the domain, namely in the highest peaks and in Mediterranean regions. Large uncertainty is thus associated with the E-OBS temperatures in the Alpine region and its surroundings since it is primarily conditioned by station density. Thus, a kilometrescale gridded dataset covering the entire Alpine domain at 5 km resolution was also considered. However, the obtained results are similar to those from the E-OBS as a reference. This similarity in the end could imply a small impact of the interpolations onto the final DAV value. The description of maximum temperature is considerably better than minimum temperature, this can be related to the misrepresentations of snow depth, melt and cover  (Terzago et al.
The lack of soil moisture not only contributes to a lower frequency of convective precipitation but also an overestimation of the higher temperature extremes. Hence the negative added value in maximum temperature above the 90th percentile. When looking from a spatial perspective, the finer resolution of CPRCMs allows a higher spatial detail of temperature and other variables, often correcting the negative impact of intermediate resolutions and could in the end add value, even when interpolated to the 0. 1° (~ 11 km) resolution of the E-OBS or the 5 km from the EMO-5 gridded datasets. Although one can see a loss in most regions and models, there is added value in e. g. , southern European coastal regions. These consistent gains over the Mediterranean coasts hint at improved coastal dynamics, derived from the increase of resolution, either from a betterresolved topography near the coast or by a better representation of the ocean-land contrasts on atmospheric flow.
2021; Careto et al. 2022a ). The improvement of the representation of convective cells in tandem with a better description of snow accretion, soil moisture and groundwater would enhance our understanding of the processes that would contribute to the development of better parameterisations and enhance the reliability of the simulations. Notwithstanding, the improvement of the spatial representation of local terrain, atmospheric circulations and land-ocean-atmosphere interactions suggests that the ambition to use increasingly higher-resolution simulations is worth pursuing. EU-FP6 project UERRA (http:// www. uerra. eu) and the Copernicus Climate Change Service, and the data providers in the ECA&D project (https:// www. ECAD. eu). The authors acknowledge the WCRP-CORDEX-FPS on Convective phenomena at high resolution over Europe and the Mediterranean (FPSCONV-ALP-3) and the research data exchange infrastructure and services provided by the Jülich Supercomputing Centre, Germany, as part of the Helmholtz Data Federation initiative.
Pedro MM Soares and Rita M Cardoso were supported by national funds (PIDDAC) through FCT (Fundação para a Ciência e a Tecnologia, I. P. /MCTES, Portugal) under project LEADING (PTDC/ CTA-MET/28914/2017), and project UIDB/50019/2020. João Careto is supported by the Portuguese Foundation for Science and Technology (FCT) with the Doctoral Grant SFRH/BD/139227/2018 financed by national funds from the MCTES, within the Faculty of Sciences, University of Lisbon. The ICTP acknowledges the Consorzio Interuniversitario per il Calcolo Automatico dell'Italia Nord Orientale (CINECA) center (Bologna, Italy) for super-computing support. UHOH simulations were carried out at the supercomputing center HLRS in Stuttgart, Germany. UCAN simulations have been carried out on the Altamira Supercomputer at the Instituto de Física de Cantabria (IFCA-CSIC), a member of the Spanish Supercomputing Network, and supported by the Spanish R&D Program through project INSIGNIA (CGL2016-79210-R), co-funded by the European Regional Development Fund (ERDF/FEDER).
AUTH acknowledges the support of the Greek Research 603 and Technology Network (GRNET) High Performance Computing (HPC) infrastructure for providing the computational resources of AUTH simulations (under project ID pr009020_thin) and the AUTH Scientific Computing Center for technical support. CICERO simulations were performed using the NN9188K project account on resources provided by UNINETT Sigma2 -the National Infrastructure for High Performance Computing and Data Storage in Norway. BCCR simulations were performed on resources provided by UNINETT Sigma2-the National Infrastructure for High Performance Computing and Data Storage in Norway (project NN9280K). The HCLIM simulations were supported by resources from the European Centre for Medium-Range Weather Forecasts (ECMWF) and the Swedish National Infrastructure for Computing (SNIC) at the Swedish National Supercomputing Centre (NSC) at Linköping University.
FZJ and WEGC acknowledge the computational resources granted by the John von Neumann Institute for Computing (NIC) and provided on the supercomputer JURECA at JSC through the grant JJSC39. WEGC additionally acknowledges the Vienna Scientific Cluster (VSC) projects 70992 and 71193, the cooperation project GEOCLIM Data Infrastructure Austria, funded by the Austrian Education, Science and Research Ministry (BMBWF) as well as the Austrian Climate Research Programme (ACRP) of the Klima-und Energiefonds projects HighEnd: Extremes, EASICLIM, and reclip: convex (nos. B368608, KR16AC0K13160, and B769999, respectively). SMHI and KNMI acknowledge support from the EU Horizon 2020 EUCP project (grant no. 776613) and SMHI acknowledges also support from the Formas EDUCAS project (grant no. 2019-00829). UIBK and ETH acknowledge PRACE for awarding access to Piz Daint at the Swiss National Supercomputing Center (CSCS, Switzerland), and the Federal Office for Meteorology and Climatology MeteoSwiss, the Swiss National Supercomputing Centre (CSCS), and ETH Zürich for their contributions to the development of the GPU-accelerated version of COSMO.
Data availability The datasets generated during and/or analysed during the current study are not publicly available but will be published in the near future. Still, the data are available from the corresponding author upon reasonable request. Open Access This article is licensed under a Creative Commons Attribution 4. 0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Understanding the paleoecology of species is fundamental to reconstructions of paleoecological communities, analyses of changing paleoenvironments, and the evolutionary history of many lineages. One method of establishing paleoecology is through comparing the morphology of extant analogs to extinct species; this method has been applied to many vertebrate groups using predictive linear models, but has been rarely applied using invertebrate taxa or non-linear frameworks. The ant fossil record, which frequently preserves specimens in three-dimensional fidelity, chronicles putative faunal turnover during the Late Cretaceous and into Cenozoic. The earliest fossil species comprise enigmatic stem-groups that underwent extinction concomitant with crown ant diversification. Here, we apply a wide-ranging extant ecomorphological dataset to demonstrate the utility of Random Forest machine learning classification in predicting the ecology of stem-group "hell ants".
Remarkably, hell ants possess an array of horn-like cranial appendages that have been directly observed to facilitate solitary predation through fossil remains  (Barden et al. 2020) : haidomyrmecines captured prey individually by articulating their mandibles against their horns. Hell ants have been hypothesized as arboreal predators, considering the potential difficulty of substrate manipulation with their vertically-aligned mandibles, and frequent preservation in amber, potentially indicating close proximity to tree resin  (Dlussky 1996; Barden and Grimaldi 2012; Lattke and Melo 2020). What functional niches did the earliest ant predators occupy and how does this occupation compare with extant lineages? Here, we use a wide-ranging extant morphometric dataset spanning over 160 species that has previously demonstrated a quantitative link between morphology and ecology to predict the paleoecology of several hell ant species.
Methods We applied a supervised machine learning approach by training multiple random forest models on a comprehensive ecomorphological dataset of extant ant taxa. Briefly, for each ecological niche aspect, we constructed a total of four overarching models trained on four subsets of extant data to evaluate alternative predictive schemes based on body size and dataset completeness in anticipation of partial fossil specimens: i) a complete morphometric dataset comprising raw measurements; ii) a complete morphometric dataset comprising size-corrected ratio measurements; iii) a subset of the complete dataset comprising raw measurements; iv) a subset of the complete dataset comprising size-corrected ratio measurements. We then used each model to predict ecological niche aspects based on two alternate versions of input fossil data: one including measurements based on assumed homology of haidomyrmine ants and another assuming haidomyrmecine measurements corresponding with functional analogs of extant ants.
Training dataset We developed a predictive machine learning model using a training dataset of extant ant morphometrics with sampling following  Sosiak & Barden (2021). Our extant dataset spans 15 subfamilies, 113 genera, and 167 species, including three specimens per species where possible, and measuring as many conspecifics as were present in museum collections otherwise. Polymorphic species are represented by the media caste, and species with specialized castes are represented by non-specialized "minor" workers. There is currently no evidence for specialized hell ant worker castes and specialized worker castes are not a synapomorphy of crown ants. Our morphometric sampling comprised linear measurements of 12 cephalic traits and five post-cephalic traits. Most sampled traits have been previously linked to ecology  (Weiser and Kaspari 2006; Yates and Andrew 2011; Gibb et al.