dataesr/scientific-paragraphs-categorization huggingface dataset:
Hydraulic traits data We extracted values from the recently updated xylem traits database  32  for minimum water potential recorded in the xylem (P min ) and water potential at the 50% and 88% loss of conductivity (P 50 and P 88 ) for 685, 1,376 and 735 species, respectively, measured in stems of mature individuals. The P 50 and P 88 included only observations with values <-0. 5 MPa that originated from S-shaped vulnerability curves. Taxonomic standardization was carried out as described earlier. The P min estimated as the absolute minimum xylem water pressure recorded for a given species can be prone to biases  54  , so we tested for its relationship with soil minimum water availability and maximum vapour pressure deficit within the distribution of the species, which were considered to be among the main environmental drivers of its variation.
To summarize edaphoclimatic information, we implemented a principal component analysis on species mean values for the whole set of variables using the princomp function from the stats R package  63. The first five principal components explained 82. 3% of the variance and were used in further analyses. Additional edaphoclimatic information required in some analyses (see the last two sections in Methods) was downloaded separately. This included the aridity index  64  , historical maximum temperature for 1970-2000  58  , as well as biome identity  65  and pixel-level PFT (ERA Copernicus 2019 land cover v. 2. 1. 1)  66. All these edaphoclimatic layers were aggregated to a 5 km 2 resolution for further use with the raster R package  61.
Mortality database We used a global database on forest die-off events related to drought and/or heat  1, 3  , which is an updated and geographically referenced version of the ref. 1 dataset. This new database was a spatial points data frame covering 1,303 mortality events records (Supplementary Fig. 3c ), with documented affected species in each instance (>400 tree species worldwide). Taxonomic standardization was carried out for species in the mortality database as described above.
Phylogenetic information To include species phylogenetic information, we used a newly derived genus-level phylogeny covering 3,488 genera  67  to construct a phylogenetic distance matrix between taxa using the cophenetic. phylo function of the ape R package  68. The distance matrix was used to calculate phylogenetic principal coordinates values for each genus using the pcoa function of the ape R package  68. Then, coordinate values were assigned to each species  68. Overall, we generated a dataset covering 44,901 species with complete edaphoclimatic and phylogenetic data and some sparse data on hydraulic traits distributed throughout the phylogeny. We also constructed a species-level phylogeny using the V. PhyloMaker R package  69  matching our species list. We used the species-level phylogeny only for plotting purposes because it contained many polytomies and because genus-level approaches can be considered more reliable, especially for tropical clades where species misidentification can be an issue  70.
Hydraulic traits imputation We used random-forest models as implemented by the missForest R package  33  to predict and impute species-level P min and P 50 values for the 44,901 woody plant species for which we had distribution data. This predictive framework was chosen on the basis of previous results that showed a strong relationship between these traits and edaphoclimatic and phylogenetic data  31. Before performing the imputations, we tested the predictive performance of a set of models including different combinations of phylogenetic principal coordinates, edaphoclimatic principal components and including or excluding major evolutionary affiliation (angiosperms versus gymnosperms). We built models that predicted one trait at a time or both (P min and P 50 ), within the same model (in the latter case, trait covariation was explicitly considered).
Hydraulic metrics of species assemblages To plot hydraulic metrics for species assemblages, we first spatially referenced species-level imputed data for 44,901 species using their spatial range distribution  34  (Supplementary Fig. 3a, b  to see species range distribution coverage for imputed and observed traits data, respectively). Spatial projections were implemented by assuming fixed trait values at the species level (as we expect intraspecific variability to be much lower than interspecific variability for hydraulic traits)  [71] [72] [73]. Then, we aggregated trait values for species with overlapping distributions at the pixel level by calculating their mean, minimum and variance as a measure of functional variability by using the fasterize function of the fasterize R package  74  and the rasterize function of the raster R package  61  in the case of the variance.
However, we also provide maps excluding land cover categories without woody vegetation (using Copernicus, the land cover map previously referred to as a reference)  66  (Supplementary Fig. 10 ). We also spatially aggregated cross-species P 50 and HSM standard deviations by calculating the mean from the 100 iterations of the predictive model including all species (Supplementary Fig. 7a, c, e ) and excluding the 20% of species with observed trait data in each iteration (Supplementary Fig. 7b, d, f ). Then, we report two measures of model uncertainty aggregated at the spatial scale: the first one showing the uncertainty of the predictive model at the species level and the second one the uncertainty linked to the identity of the species represented in the training data used. The uncertainty due to the identity of the species used to train models is higher than the model uncertainty (Supplementary Fig.
Assessing the predictive capacity of hydraulic traits First, we tested the relationship between imputed species-level HSM values and the presence-absence of observed mortality as well as the number of mortality events recorded per species as reported in the global mortality database  3. We used generalized linear models through the glm function of the stats R package  63  , setting the family parameter to 'binomial' in the first case and to 'Poisson' in the second one. To see the effects of angiosperm versus gymnosperm affiliation in this relationship, we included the major evolutionary affiliation as an explanatory factor interacting with HSM. As the number of species without observed mortality was much higher than the number with observed mortality, we randomly selected the same number of species without observed mortality events to match the number of species with mortality events (that is, 482).
We repeated this procedure 100 times and averaged the results in both cases. To explore the relationship between the spatial projection of hydraulic metrics and mortality occurrence as reported by the global DIM database 3 , we used binomial generalized linear models with the glm function of the stats R package  63. We kept one mortality event per square kilometre, reducing the number of geographical points with observed DIM from 1,303 to 882 to avoid over-representing areas with a higher sampling effort. To assess the degree of spatial autocorrelation of models, we performed Mantel tests on the residuals of all models using the function mantel. rtest of the Ade4r package  75. The spatial autocorrelation was <0. 06 in all cases. The response variable in our models was mortality occurrence (1 for pixels with at least one mortality event observed and 0 for the same number of randomly sampled pixels without observed mortality).
Trend significance was tested by using the emmeans R package  76  (Supplementary Table  5 ). Each model was run 100 times using a different set of background points and pseudo-R 2 values were calculated using the rml R package  77  (Supplementary Table  2 ). Test AUC values were also calculated using the dismo R package  78  following a cross-validation procedure with 80% of the data to train and 20% to test. All models were rerun including aridity index values extracted from mortality and background points as a covariate to test whether trait effects remained significant when the climate was considered, which was the case. To check for variable significance, we implemented analysis of variance tests using the anova function from the stats R package  63  (Supplementary Table  3  gives the mean results calculated from 100 iterations in each case for models including aridity index as a covariate).
Projecting mortality risk using maximum entropy models We used maximum entropy models  35  as implemented by the dismo R package  78  to predict and project DIM risk at the global scale. We used this methodology instead of the previous binomial generalized linear models as it accounts better for presence/background point data under a predictive framework. This allowed us to better characterize the background by including more background points than presences, a procedure not recommended with generalized linear models 80. Moreover, this technique presents higher predictive performance than generalized linear models because of its capability to account for nonlinearities and multiple interactions between predictors 81. Three types of models were run: type a using only functional type and biome distributions as predictors, type b as in type a plus continuous edaphoclimatic variables and type c as in type b plus the projected hydraulic metrics as predictors.
6 ). We made sure to include both points with observed mortality and background points in all cases by sampling the 80% and the 20% in each of these groups separately and then unifying the datasets, following previous implementations 82. Finally, a single model trained using all observations was implemented for model types a, b and c (see earlier) and used to project mortality occurrence probability geographically (Fig. 6 ). Variable importance was assessed by its relative (percentage) contribution to the fit of the models as generated by the maxent jack-knife procedure, which compares the training gain for each variable in isolation to the training gain of the model with all variables (Fig. 6 ). Permutation importance was also calculated for each edaphoclimatic variable by randomly permuting presence and background values, re-evaluating the model and calculating the resulting drop in training AUC, normalized as a percentage (Fig.
Acknowledgements This work was supported by the  Spanish government  via competitive grants  FUN2FUN  (no. CGL2013-46808-R ),  DRESS  (no. CGL2017-89149-C2-1-R ) and  TRACES  (no. PID2021-127452NB-I00 ) funded by  MCIN/AEI/10. 13039/501100011033 ; and by grant no. 2021 SGR 00849  funded by  AGAUR. P. S. -M. acknowledges an  FPU predoctoral fellowship  from the  Spanish Ministry of Science, Innovation and Universities  (grant no. FPU18/04945 ). J. M. -V. benefited from an  ICREA Academia  award. R. A. S. is supported by  Fondecyt-Iniciacion /2020 1100967 ,  ANID, Chile  and grant no. ANID/BASAL FB210006. J. -C. S. and W. -Y. G. thank the  VILLUM FONDEN  for support via  VILLUM Investigator  project ' Biodiversity Dynamics in a Changing World'  (grant no. 16549 ). J. -C. S. also considers this work a contribution to Center for Ecological Dynamics in a Novel Biosphere (ECONOVO), funded by  Danish National Research Foundation  (grant no.
Knowledge based system for sustainable disassembly Some researchers studied the use of the knowledge-based system in order to promote a sustainable disassembly. For example:  (Poschmann, Brüggemann and Goldmann, 2021)  presented a cognitive robotic system is presented which is capable of integrating individual product lifecycle data to decide upon the actual degree of disassembly in regard to the most suitable EoL utilization option. (Terazono et al. , 2012)  studied the advantages of sharing the recycling knowledge among stakeholders of the same sector. (Babbitt et al. , 2020)  presented a comprehensive database of bill of material (BOM) data describing the mass of major materials and components contained in 95 unique consumer electronic products. Also, in order to optimize the waste management,  (Lederer et al.
are stored in an independent database related to each product and constantly filed and updated during the products lifecycle by the manufacturer that is supposed to intervene in all the maintenance and repairing operations. Once the data of the product is extracted, the digital model linked with all this data is created. Then all the data related to the other life cycle steps of the products are extracted from the knowledge based in order to proceed the first environmental evaluation. Step 3: Using all the data available in the system (all lifecycle products data and process data as shown in the Figure  2 ), the first environmental evaluation is proceeded without the EoL step in order to detect the components with the most significant environmental impact independently of the EoL scenarios.
ICED23 As shown in the figure  3 , KBDTw system has three main contributions: the data visualization, EI simulation and decision aid. The data visualization: In the step of products' data extraction, the KBDTw system provides a complete visualization of the products information extracted from the product database and the knowledge based and the digital model. EI simulation: The KBDTw provides the environmental impact assessment simulation, which consists of LCA of the product without and with different EoL scenarios. Decision support: the system helps with the decision support by detecting the components with the most significant environmental impacts and most sustainable disassembly scenarios.
The use of KBDTw in the EoL of the internet boxes The KBDTw will allow having all the necessary information in order to choose different valorisation strategies and disassembly scenarios after the second sorting area (Figure  5 ). Which means that instead of the obsolete boxes going directly to the recycling without favouring the use of their functional parts and components, the remanufacturer will have all the necessary data related to all the lifecycle of the internet box in order to identify the parts still reusable and if it is necessary to reuse them. Following the KBDTw steps explained in the third section of the paper. The EoL of the internet box will go as follow: After the request of the disassembly step, the internet box is firstly identified by a QR code and all the data stored during its lifetime (Table  1 ) is extracted.
Once the data of the product is extracted, the digital model linked with all this data is created. Then all the data related to the other life cycle steps of the products are extracted from the knowledge based in order to proceed the first environmental evaluation. Using all the data available in the system (e. g. components, materials, middle life data, etc. ), the first environmental evaluation is proceeded without EoL. The environmental evaluation of the results shown in the figure  7  is proceeded with a LCA software called SimaPro (Figure  6 ), using the databases "Ecoinvent 3" and "Methods" and "IMPACT 2002+" method. In order to conduct this environmental evaluation, only 4 steps of the internet box lifecycle were taken into consideration in this study (raw material extraction, manufacturing, distribution and the use).
That said, we do not yet have practical experience with an implementation of this type system. Tes offers a strong type soundness guarantee: a well-typed program cannot crash and cannot halt due to an unhandled effect. To prove this fact, we follow a semantic approach that has become popular in the last few years  [1, 20, 19]. We introduce TesLogic, a novel variant of Separation Logic, constructed on top of Iris  [16] , which allows reasoning about programs in the presence of effects and handlers, multi-shot continuations, and dynamic allocation of effect labels. We prove that this logic is sound, and we provide an interpretation of Tes's typing rules in terms of TesLogic's reasoning rules. All of our results are formalized in Coq, and our Coq formalization is available  [36]. In summary, the main contributions of this paper are the design of Tes, a type system for TesLang, a λ-calculus equipped with general references, effect handlers, and dynamic allocation of effect labels, and a proof of type soundness, which is carried out via a semantic interpretation into a new program logic, TesLogic.
Syntax and Semantics We introduce TesLang, a calculus with mutable state, effect handlers, multiple named effects, dynamic allocation of effect labels, and multi-shot continuations. The operational semantics of this calculus allows a continuation to be invoked several times. With respect to this semantics, the type system presented in this paper ( §4) is strongly sound: it rules out all runtime errors ( §5). With respect to a dynamic semantics where invoking a continuation twice causes a runtime failure, such as the semantics of OCaml 5, our type system would be weakly sound, because it does not rule out this kind of runtime failure. Ensuring that every continuation is invoked at most once would require an affine type system and is beyond the scope of this paper. We note that an affine program logic, such as Hazel  [35] , can guarantee that no continuation is invoked twice, therefore can guarantee strong soundness even in the presence of one-shot continuations.
Metatheory In this section, we present the general architecture of the proof of our type soundness statement (Theorem 3), which states that, if a closed program e is well-typed, then e is safe: that is, e may diverge or terminate with a value, but cannot perform an unhandled effect. Full details are found in our Coq code  [36]. Our first step is to interpret our typing judgments as semantic typing judgments. A semantic typing judgment Ξ | ∆ | Γ ⊨ e : ρ : τ is a logical assertion stating that substituting certain values for the free variables of e yields a closed program that meets a certain specification. To fill in the details, one must define precisely which values may be substituted and what specification is met. To do so, we introduce TesLogic, an extension of Iris  [16] , an expressive Separation Logic.
Iris's base logic has no built-in support for effects and handlers, but allows constructing a program logic with such support. de Vilhena and Pottier define such a logic, Hazel  [35]. Because Hazel is tailored for unnamed effects and one-shot continuations, we cannot re-use it. Nevertheless, in the design of TesLogic, we do rely on one of Hazel's key features, protocols. A protocol Ψ describes a service on which the handlee can rely and which the handler must implement. Mathematically, it is a binary relation between a value v, the payload of the effect, and a predicate Φ, the precondition of the continuation for this effect. A typical example of a protocol is the pre/post protocol  ewp v ⟨E⟩{Φ} ≜ Φ(v) ewp (eff ℓ v K) ⟨E⟩{Φ} ≜ ∃Ψ. (ℓ, Ψ ) ∈ E * (↑ □ Ψ ) v (λw. ▷ ewp K[w] ⟨E⟩{Φ}) ewp e ⟨E⟩{Φ} ≜ ∀ σ.
Our (unary) semantic model ( §5) errs on the side of admitting more constructs and fewer equivalence laws. In future work, it would be interesting to propose a (binary) semantic model that admits fewer constructs and validates more laws, so as to prove that Tes without dynamic-wind validates Zhang and Myers's law  [41, Example 1]. Despite their previous studies of coercions  [4, 5] , Biernacki et al. [6]  argue against coercions, which they deem impractical for real-world programming, and propose a type system for a language that supports lexically scoped handlers only. They present two semantics for this language:  (1)  an open semantics, where effect names are not substituted with labels, and where evaluation is defined among open terms in a capture-avoiding way; and (2) a generative semantics, where effect names are substituted at runtime with effect labels, as in TesLang.
Data analysis In their standalone operations, both MAGIC and LST-1 use independent analysis chains. The MAGIC data analysis is based on MARS  (Moralejo et al. 2009; Zanin et al. 2013) , a C++, ROOTbased library, and a package of analysis programs. The raw data are stored in a custom binary format and the data are generated at each processing step are stored using ROOT containers. On the other hand LST-1 is using cta-lstchain (Lopez-Coto et al. 2022), a Python-based analysis library exploiting ctapipe  (Kosack et al. 2022). The LST-1 raw data consist of pixel-wise waveforms and auxiliary information. They are stored in a zfits format  (Pence et al. 2012; Lyard et al. 2017 ) and processed data are stored in HDF5 files  (Nozaki et al. 2020). For the data obtained by observations, we performed the first stages of the data processing, namely the signal extraction from individual pixel waveforms and the calibration of the resulting images to photoelectrons (p.
e. ) and individual pixel timing, with the specific software of each instrument. Then the MAGIC data are converted into HDF5 format, compatible with the LST-1 data using the dedicated ctapipe_io_magic package 1. The rest of the analysis chain is performed with the magic-cta-pipe 2  package using lstchain and ctapipe methods. In particular, the magic-cta-pipe contains analysis scripts to, for example, apply the same image cleaning as in the MARS package, but within a ctapipe-like environment and to match the events produced by the same shower in the three telescopes. All the other higher-level analysis steps are performed with the magic-cta-pipe package as well, with the help of other modules for specific tasks (e. g. , pyirf,   gammapy,  Deil et al. 2017 , for flux estimation). For MC simulations, all the processing (including the calibration) is instead performed with magic-cta-pipe.
The analysis chain presented in this paper is referred to as the MAGIC-ctapipe (MCP) chain (and summarized in Fig. 2 ). Such a scheme allows us to take advantage of the automatic processing of the bulky, early stages of data and exploit the already implemented low-level calibration corrections (see e. g. ,  Sitarek et al. 2013  for the case of  MAGIC and Cassol et al. , in prep. for LST-1). At the same time, it allows for the utilization of state-of-the-art software developed for CTAO, and in turn, the newly developed tools can also be easily applied for CTAO analysis in the future. After the initial image cleaning (see  Aleksić et al. 2016b ), the images are parametrized using the classical approach of  (Hillas 1985 ) and a quality cut on "intensity" is applied (i. e. , the total number of p.
2001; Aleksić et al. 2010)  were computed. The training was done using a random forest (RF) method  (Breiman 2001) , implemented in the scikit-learn package  (Pedregosa et al. 2011). The RF regressors used for energy and DISP estimations used 150 estimators, a maximum tree depth of 50, the squared error criterion for selection of the best cut from all the parameters at each step and division of leaves down to a single event. The RF classifier used for gamma-hadron separation employed 100 estimators with a maximum depth of 100. In this case, the RF branching was done using the Gini index criterion, but at each step, only the square root of the total number of parameters is randomly selected. Individual telescope estimates are based on the Hillas parametrization (intensity, length, width, skewness, kurtosis, and time slope computed along the main axis of the image, and fraction of total image intensity in the two outermost rings of pixels) in the particular telescope.