dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2020) , half of these being linked to specific geographic locations. Plant mechanistic models typically focus on growth rates as the primary metric of performance  (Duursma & Medlyn, 2012; Schouten et al. , 2020) , though phenology is also commonly used  (Chapman et al. , 2014). However, there are still very few measurements of solar absorptivity (but see  Gates, 1980). Relative to plants, databases of functional traits for animals are less consolidated and extensive. Biophysical heat and water flux calculations of animals require estimates of body size, area, and shape as well as solar reflectance and emissivity. For endotherms, insulation properties (i. e. , density, length, diameter of hairs)  (Campbell & Norman, 1998; Gates, 1980)  are required to estimate thermal conductivity of insulation, such as pelage or plumage, or conductivity can be measured directly from specimens  (Porter et al.
, 1994; Riddell et al. , 2021). Relevant physiological functional traits include basal or standard metabolic rate, cutaneous resistance to water loss, target body temperature, thermal tolerances, and thermal optima. Behavioral traits include body temperature thresholds for thermoregulation, including thermoregulatory mode (or accuracy) and desiccation avoidance  (Clusella-Trullas & Chown, 2014; Kearney, Shine, et al. , 2009; Riddell et al. , 2018; Sears et al. , 2016). Gathering information on so many traits is challenging since functional trait databases for animals typically focus on one type of trait  (Herberstein et al. , 2022; Myhrvold et al. , 2015) , but more often, these traits are published for groups of animals  (Bennett et al. , 2018; Clusella-Trullas & Chown, 2014; Grimm et al.
, 2014; Le Galliard et al. , 2021; Madin et al. , 2016; Oliveira et al. , 2017). In addition to consolidated databases, there is a wealth of animal functional trait data available in the published literature, particularly for well-studied groups. For example, a literature search with terms relating to different types of relevant traits for lizards and snakes (see Appendix S1) identified 9029 unique papers. Papers focused on thermal physiology were most common, followed by hydric physiology, morphology (excluding body mass), metabolism, and then studies that examined both behavioral thermoregulation and thermal physiology (Figure  3b ). Although the availability of functional trait data is rising, there is much room for improvement in how these data are collected and collated, and methodology can have a substantial impact on trait values.
Thermal tolerances can exhibit important variation due to acclimation effects, the rate at which temperature changes, or the duration in which organisms are exposed to a temperature  (Pintor et al. , 2016; Sunday et al. , 2019). Similarly, functional traits can vary depending on whether they are measured under constant or (more natural) fluctuating conditions  (Morash et al. , 2018; Niehaus et al. , 2012). Species' traits can also exhibit substantial variation within and across populations, across developmental life stages, or in response to environmental cues over time (i. e. , phenotypic or behavioral plasticity)  (Moran et al. , 2016). Ensuring that individual-level measurements and relevant metadata are recorded in functional trait databases will help ensure that trait data are available and can reliably be used in biophysical modeling.
For example, georeferenced trait data can be combined with environmental data to assess the environmental sensitivity of certain traits, while museum collections can be used to quantify spatial and temporal variation in traits  (Briscoe et al. , 2015; Gardner et al. , 2019). Alternatively, sensitivity analyses can assess the impact of variation in a particular trait or identify the functional traits that most strongly influence estimates of performance  (Augusiak et al. , 2014; van der Vaart et al. , 2016). A single general database of functional traits for biophysical (and metabolic) modeling would greatly enhance the uptake of the methods, make data deficiencies clear, and advance the study of functional traits in general  (Kearney, Jusup, et al. , 2021).
| Software ecosystems The use of statistical programming tools-largely R, sometimes Python-has become ubiquitous for researchers of global change biology  (Lai et al. , 2019). This competence has developed in tandem with the emergence of vast software ecosystems that provide the many interoperable open-source packages we combine to process data and build models  (Hoving et al. , 2013; Plakidas et al. , 2017). It is rare for researchers to write their own statistical algorithms: instead, statistical modelers combine freely available tools to analyze their specific problem using high-level model definitions. In statistics, modelers stand on the shoulders of thousands, across varied disciplines, who have published their tools on CRAN  (Hornik, 2012)  and contributed to R's software ecosystem.
Biophysical modeling requires researchers to write a different kind of software to the statistical scripts many are accustomed to. The differential equations of biophysical models often have hetero- A potential solution to this problem is the Julia language  (Perkel, 2019; Schouten et al. , 2020 Schouten et al. , , 2022)). Julia has a rapidly growing, highly intercompatible software ecosystem targeted at scientific computing, differential equations, and model optimization. Its code is similar to dynamic languages like Python and R. However, it compiles packages and user scripts down to machine code at runtime giving performance comparable to Fortran. As an example of this potential, Julia is used for large-scale biophysical modeling by the Climate Modeling Alliance (CliMA). The CliMA project combines model components maintained in separate repositories for their ocean, land, and atmospheric models.
The Ecological Forecasting Initiative has developed comprehensive infrastructure for near-term ecological forecasting that could readily be adapted for the mechanistic approaches that benefit longer term forecasting  (Dietze et al. , 2018 (Dietze et al. , , 2021)). Central to the computational infrastructure are databases with historical biological data for model testing and comparisons. The availability of relatively high-resolution historical climate and paleoclimate datasets means that it is now possible to revisit or reinterpret previous field studies or past extinctions, including using these data for model testing  (Mathewson et al. , 2017; Morris et al. , 2022; Wang et al. , 2018). An integral part of these future workflows will be generating and mapping realistic estimates of uncertainty, for example, due to underlying climate forecasts, traits (including behavior), or model structure  (Briscoe et al.
| VIS I ON FOR FUTURE OF TACK LING G LOBAL CHANG E B IOLOGY PROB LEMS Our long-term vision for the future of biophysical modeling involves researchers, trained in the physical principles of biophysical ecology, using modular and flexible methods, and using data compiled in a standardized functional trait database, to answer diverse questions in global change biology. A barrier to realizing this vision is that funding calls for projecting the biodiversity responses to climate change often seek applied projections for many species, analogous to that feasible with statistical models (i. e. , correlative species distribution models). In contrast, furthering models built around biophysical ecology will require extensive basic research and investment in the initiatives outlined above. We argue such efforts are nonetheless essential to adequately projecting biological responses to environmental change.
Fire propagation simulation setup The discretisation of the Casa di Diana building, 34m ⇥ 48m ⇥ 12. 6m, with a grid size of = 10cm, led to a mesh containing 20. 5M vertices. The geometric quality of the isotropic mesh has been assessed by cross-validating the ratio D ⇤ / , where D ⇤ is the characteristic size of the simulated fire  [10]. The 3D geometric model is converted into a FDS readable input model thanks to the open-source Python based BlenderFDS add-on 3. It allows the conversion of complex 3D geometrical model into FDS readable geometry through a voxelization procedure, Figure  3 , while maintaining a complete authority on the other FDS parameters.
Simulation results In this section, we gather a few numerical results to assess our protocol to model the fire propagation in an (ancient) building of archeological interest. Most following plots have been obtained using the open source Smokeview software  [4]. Smokeview has an emphasis on realistic smoke and fire visualization, but contains more classical method such as 2D or 3D contours plots of flow field data. Pascal Frey ⇤, ‡ , Nicolas Leys †, ‡ , & Clément Scherding ‡ Archaeological specifications. In order to ignite properly the fire, a square patch generating 2MW of heat has been added in a room on the second floor of the Casa di Diana model. This setting may be a good approximation of the accidental departure of a fire from a stove, for instance. A preliminary stage was to investigate which model was more suitable for fire propagation: with or without vents in the ceiling and floor of rooms.
METHODS We first motivate the DREDge algorithm using the decentralized registration approach  [13]  that it builds on in section 2. 1. Then we describe several additions that enable DREDge to estimate drift in multiband electrophysiology data in a robust, efficient, and online manner in sections 2. 2,2. 4,and2. 5. In section 2. 6, we describe the procedure for realigning electrophysiology data through interpolation, adjusting for the shifting signal after motion estimation. Finally, we summarize all of these steps in algorithmic pseudocode in section 2. 7.
Algorithmic details We summarize the above pipeline to estimate motion drift in electrophysiology data as the DREDge algorithm whose pseudocode details can be found in Algorithm 1. Note that the spatiotemporal signal matrix R ∈ R D×T can either be based on the AP band or the LFP band, resulting in two versions of the algorithm that we refer to below as DREDge-AP and DREDge-CSD. Algorithm 1 denotes the "batch" version of motion estimation. In large data cases such as in chronic recordings or in real-time applications, Algorithm 1 can serve as a subroutine for an online estimation of motion as described in section 2. 5 where smaller time chunks of signal matrices R act as input in a streaming fashion.
Compute optimal displacements and correlations Allocate T × T matrices D, C for 1 ≤ t, t ′ ≤ T do D tt ′ ← arg max ∆y corr(r t (y), r t ′ (y + ∆y)) C tt ′ ← corr(r t (y), r t ′ (y + D tt ′ )) end for Compute the adaptive correlation threshold θ θ ← quantile q {C t,t+1 : t = 1,. , T -1}   detections in interpolated data 188 39 1063 Fig. 4. Checking the drift estimate using drift-corrected spike localizations  [16]  and interpolated waveforms. (a) Localizations of spikes detected in the Pt. 02 dataset. (b) Spike localizations shifted according to a drift estimate (DREDge-CSD in Fig. 2. a); green dots in (a) and (b) represent the same events, corresponding to spikes in a well-isolated cluster in (b) isolated by manually thresholding amplitude and selecting a rectangular region in x, z. (c) Spike localizations computed from events detected in a version of the recording which was interpolated to correct for drift using the SpikeInterface framework  [11].
RESULTS Experiments were carried out in three datasets: a mouse dataset with induced motion (dataset1 from  [2] ), and two human datasets (Pts. 02 and 09 from  [6] ). The mouse dataset is characterized by a slow (period ∼ 100s) and shallow (tens of microns) induced triangle wave drift, while the human datasets are characterized by large (hundreds of microns) and fast (sub-second) drift driven by heartbeat and breathing patterns. Comparisons were carried out against the registration algorithm in Kilosort 2. 5, which performs well on the same mouse dataset as previously shown in  [2] , but fails on the human datasets (see Fig. 2. (a, c )). Online methods used 10 4 samples per chunk. Due to the characteristic noise present in unsorted spiking data, the AP methods (KS and DREDge-AP) shown in Fig.
DISCUSSION Here, we present an online decentralized algorithm, DREDge, to track drift in electrophysiological recordings from the brain using Neuropixels probes. We demonstrate the applicability of tracking the movement of the neural signal relative to the probe with this approach in three different data sets, two recordings in the human cortex  [6]  and one in mouse  [2]. DREDge is shown to work using as input both action potentials, representing the activities of single neurons, as well as the local field potential, representing the summed activity of hundreds to thousands of neurons. We develop methods to adapt this algorithm to the structure of individual datasets, improving the robustness and ease of use of this tool. Finally, we suggest that DREDge could be used to improve the clustering of spike localization features for applications in spike sorting.
DREDge allows researchers to move forward in using such precious data to understand human brain activity. A major question is whether after applying the tracked motion to the signal, there are changes that are not present in the original signal. Our data suggest that applying DREDge correction to motion-degraded spike and local field potential data has the potential to accurately reconstruct these electrophysiological signals, while not altering their spatiotemporal profiles in datasets without significant motion artefacts. However, the power of our approach is that we use both the LFP and the spiking activity in parallel to estimate the motion and, alternately, can use each, particularly the spike waveforms, to confirm that we are not adding in artefacts. Further validation could therefore make use of 2D cross-correlation to determine if new spiking waveforms were found in the corrected data sets  [6].
ACKNOWLEDGMENTS We would like to thank Yangling Chou, Aaron Tripp, Brian Coughlin, and Fausto Minidio for their help in the original data collection. We would like to especially thank the patients for their willingness to participate in this research. EV is supported by 1K99MH128772-01A1. This research was supported by the ECOR and K24-NS088568 (to SSC) and the Tiny Blue Dot Foundation (to SSC and ACP) and NIH grant U01NS121616 (to ZMW). CW, EV and LP is funded by Simons Foundation 543023, NSF Neuronex Award DBI-1707398 and the Gatsby Charitable Foundation. The views and conclusions contained in this document are those of the authors and do not represent the official policies, either expressed or implied, of the funding sources. The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
This further indicates the misfit with the (simple) standard models of kinetic resolutions as described by Sih. [17] espite the intricated reaction mechanism, we aimed to outline the effect of the temperature on the reaction system. A novel strategy, the Bayesian-like concept of 'Shape Language Modeling' (SLM, in MATLAB®) was applied to design a description for each of the investigated temperatures. SLM is a shape prescriptive approach, not a fixed mechanistic-mathematical model. [18] The method allowed us to avoid forcing the data into a model for the (uncertain) mechanism, which would be purely speculative. Instead, we describe the reaction in a prescriptive manner using sets of shape primitives for the curve fit. SLM permitted the creation of a 'function' for each of the temperature reaction setups (see Figure  S4  and S5, Supporting Information) and, consequently, allowed us to calculate the ee of the ketone at any conversion.
Conclusion We have demonstrated the temperature effect on the lactonization of bicyclo[3. 2. 0]hept-2-en-6-one 1, a model substrate for BV oxidations, by 2,5-DKCMO, a rare representative of FMNdependent BVMOs, with a greater enantiopreference at lower temperatures. The classical criterium to quantify stereospecificity, E, being not calculable with precision for this complex system, the 'Shape Language Modeling' (SLM) was introduced to reveal the temperature-dependent effect on the regio-and enantioselectivity of the biocatalyst unambiguously. Thus, we showed that SLM is an efficient tool to create prescriptive functions usefully applicable for reactions of unknown mechanistic details.
g. ,  Kelut, Merapi, Semeru). With this survey-based approach to the Semeru case, and the statistical analysis of the datasets, we pursue three objectives (Figure  1 ): 1. To define the socio-demographic, economic characteristics and origin of mixed rural/urban population to assess the social vulnerability of Semeru's population. 2. To understand to which extent livelihood and local economic resources can compensate for the risk of living on continuously active volcanoes despite an exposure to a range of hazard zones. By comparing livelihood and assets with socio-demographic characteristics, objectives 1 and 2 help define socio-economic categories of population around Semeru. 3. To correlate the knowledge of hazards with levels of risk perception and associated variables at the individual scale to assess people's awareness and preparedness, thus appraising people's behaviour in case of imminent eruption and evacuation.
3). A definition of vulnerability (UNDRR, 2014) pinpoints 'the conditions determined by physical, social, economic and environmental factors or processes, which increase the susceptibility of an individual, a community, assets or systems to the impacts of hazards'. We estimated social vulnerability by means of the SoVI index  (Cutter et al. , 2003)  with its application to the Indonesia case  (Siagian et al. , 2013). The criteria of the SoVi index include: population number and density, annual growth rate, number of children under 5 years and elders > 65 years, literacy rate among adults, poor people, and HDI  (Siagian et al. , 2013). Most of these criteria around Semeru derive from the socio-demographic dataset (Survey A) (see section 3. 1). The purpose of determining social vulnerability was to assess handicaps that prevent the population of surveyed dusun to cope with the impacts of volcanic hazards on livelihood and resources.
Surveys, respondents, and context We collected survey information in the field across 15 dusuns and within 145 blocks (Fig. 2 , ESD Fig. 1 ). We collected data from 2 to 4 households per RukunTetanga (RT, 20-25 homes), the smallest administrative unit at which respondents can be identified. Three to five RT typically form a RukunWarga (RW, a set of 50-75 buildings, mostly homes with a few offices, shops, mosques and schools). We conducted five face-to-face interviews per household in Indonesian and/or Javanese language thanks to our partners. The five surveys conducted in one sitting per household dealt with socio-demographic characteristics (Survey A), hazard knowledge (B), risk perception and associated variables (C+D2), household resources (D1), and building stock (E). As a result, 1,255 surveys were conducted, encompassing 279 respondents from 221 households, which host as many as 1,161 persons (Table  1 ).
A small number of respondents (≤ 6%) did not provide answers to several questions due to time limitations, lack of knowledge (e. g. , a few elder, very low-income, minimally educated or disabled people), and a mixture of shyness, safety concern, and/or interfering concerns during interviews. Up to 11% of respondents provided no answer to questions implying memory, illustrating that the footprint of many volcanic events (except the largest disasters) may be erased after 15-20 years. Another reason is that 20% of people arrived less than 25 years ago in the Semeru's area, and therefore did not experience severe impacts from volcanic activity. Surveys involving about 1,200 people and 300 buildings represent approximately 0. 6% to 2. 6% of the respective cohorts. However, field observations, completed by data extracted from Kecamatan reports and 2017 BPS census, involved 145 blocks from 15 dusuns, hence representing 3% to 6% of exposed people.
Statistical analysis procedure To achieve the objectives listed in section 1. 1, we explored and coded the answers extracted from five surveys to run statistical methods on a large collection of suitable variables, which allowed us to examine the characteristics of the population exposed to volcanic threats. We conducted statistical investigation using the R software and involved two main strategies. The first, using univariate and multivariate analyses, intended to explore data and extract relevant variables. The second strategy was rather oriented toward detection of relevant variables and identification of population groups based on one clustering technique. Data analysis involved three statistical techniques (Table  2 ;  Chambers et al. , 2018) : 1. Univariate analysis (UA), which is useful for exploring the dataset and computing the frequency of each of the categories of all variables.
2. Multiple Correspondence Analysis (MCA): a data analysis technique used to detect and represent underlying structures in a large set of categorical data  (Abdi and Valentin, 2007). This technique represents data as points in a low-dimensional Euclidean space, illustrated by biplots with two dimensions (e. g. , ESD Fig. 2 ). We use results from the MCA conducted on 81 variable categories stemming from five survey datasets (Table  2  and ESD Fig. 2 ). The MCA results help quantify the qualitative survey data with the aim to distinguish population groups by means of clustering techniques (see below). We selected the number of dimensions taking into account the MCA results (ESD Fig. 3 ), a scree plot showing eigenvalues following the Benzécri correction. Then we focus on the first 11 dimensions, which retain 81% of the information, and we present the contributive variables more correlated to the dimensions.
RESULTS: CHARACTERISTICS OF AT-RISK VILLAGERS We conducted statistical analyses on five survey datasets: socio-demographics, hazard knowledge, risk perception and associated variables / awareness, and livelihood and resources, including homes and belongings. The ultimate goal of our statistical investigation was to assess how these characteristics help villagers to adjust to the effects of persistent volcanic threats from Semeru. Geographical origins of respondents, parents and grand-parents, and (7) Time living in the area and ancestor arrival time.
Evaluating risk perception and associated variables amongst villagers The first goal of Surveys C+D2 (34 questions, ESD Texts 1, 2) was to evaluate how villagers perceive volcanic risk from Semeru and compare the perception with hazard knowledge. The second objective was to assess to which extent villagers would be prepared for evacuation prior to an imminent eruptive crisis. Despite some biases and a complex dataset to interpret, four contrasted groups of population (termed r for risk) stem from Surveys C+D2 (Table  5 ): The first group Ar (c. 24-26%) is aware, self-confident, and feels empowered for several reasons: they have lived in the area for >50 years and are well prepared for an eruption, which has deep roots in education, experience (having suffered from several evacuations) and local solidarity networks.
As a consequence of an extensive record of exposure to chronic threats, populations around Semeru have nevertheless promoted resilience. Such adaptive capacity provides a frame of reference for disaster-prone countries. However, to maintain a steady-state awareness and preparedness among dwellers living on continuously active volcanoes is more challenging than around their counterparts whose brief eruptions are interspersed within long-lasting periods of quiescence. Table  3. Five socio-demographic groups among 279 respondents based on the frequency analysis of the Survey A dataset. The frequency analysis used the distribution of cross tables of relevant categories such as age, education level, socio-professional category, resources and geographical origin. We also normalized the values obtained under each group of questions 1 to 2.