dataesr/scientific-paragraphs-categorization huggingface dataset:
(ref. 32) based on the Paleobiology Database  4  , which shows a transition from a bimodal Early Ordovician LBG to a unimodal Late Ordovician LBG. The authors note that this evolution results from the large increase in low-latitude biodiversity during the GOBE, in agreement with the results of our coupled climate-macroecological model (Figs. 1  and 4 ). Our results, obtained with a coupled climate-macroecological model featuring minimal biological assumptions, shed new light on the mechanisms that allowed the establishment of a modern-like LBG for the first time in Earth's history. They suggest that global climate change, rather than the two other usual suspects, paleogeography or history  48  , was decisive. They also reaffirm the contribution of a cool global climatic state to the development of a steep LBG featuring a maximum in biodiversity at the low latitudes in line with paleontological data of the Permian-Triassic transition 47.
Regional biodiversification patterns Our spatially-resolved simulations allow biodiversification signals to be compared in different paleocontinents. For comparisons, we used the brachiopod database of ref. 6. (Fig. 5 ). Our model captures the increase in fossil brachiopod biodiversity in Laurentia, Siberia and South China (Fig. 5a-c ), although with varying degrees of model-data agreement. In Laurentia and Siberia, simulated biodiversity rises too early compared to data. In South China, the general timing of biodiversity increase matches the temporal trends reconstructed based on data from the PBDB between 490 Ma and 450 Ma  6. From 440 Ma to 430 Ma, model biodiversity stabilizes while paleontological data show a large drop in biodiversity. This discrepancy in South China probably reflects the  Baltica contrasts with other continents by showing a mostly flat simulated biodiversity temporal trend, while brachiopod data exhibit a strong increase between 490 and 450 Ma (Fig.
5d ). The muted model biodiversity change arises from the combination of the latitudinal drift of Baltica to the North during the Ordovician with the long-term Ordovician cooling trend, which together make Baltica closely track a climatic belt in response to global cooling (Fig. 1 ). As a result, temperature in the Baltica epicontinental shelves remains virtually constant, and so does simulated biodiversity. Although the same factors listed above probably contribute to model-data discrepancy, it is suspected that the continental reconstruction plays a key role here. Indeed, the uncertainty in the latitudinal position of Baltica is large in the Ordovician (Supplementary Fig. 5 ). Alternative paleogeographical reconstructions  50  notably exhibit a smaller latitudinal drift of Baltica -from lower latitudes at 490 Ma to higher latitudes at 430 Ma-that could help bring model and data into closer agreement (Supplementary Fig.
Acknowledgements Calculations were partly performed using HPC resources from  DNUM CCUB (Centre de Calcul de l'Université de Bourgogne). This is a contribution of  UMR 6282 Biogéosciences Team  ' SEDS '. This is a contribution to  UNESCO  project  IGCP 735  " Rocks and the   Rise of Ordovician Life" (Rocks n ' ROL). The authors acknowledge the support of the  French Agence Nationale de la Recherche (ANR)  under reference  ANR-22-CE01-0003  (project  ECO-BOOST ; to G. B. , B. L. , T. S. , A. P. ) and of the programme TelluS of the  Institut National des Sciences de l'Univers, CNRS  (project ROSETTA; to G. B. , B. L. , T. S. , A. P. ). We also thank the  CPER programme IDEAL  (G. B. ). This work was also supported by the graduate school  IFSEA  that benefits from grant  ANR-21-EXES-0011  from the  French National Research Agency , under the  Investments for the Future Programme.
Data availability All material required to repeat the experiments, conduct the analysis of the results and generate the figures is available online  71  and assigned a https://doi. org/10. 5281/zenodo. 8307366. Biodiversity data used for model-data comparison were extracted from previously published work: global curves in Fig. 2  are after ref. 5, ref. 41. and ref. 3. Regional curves in Fig. 5 are after ref. 6.
Code availability All material required to repeat the experiments, conduct the analysis of the results and generate the figures is available online  71  and assigned a https://doi. org/10. 5281/zenodo. 8307366. The Matlab code is also available on GitHub (https://github. com/DanElie/Cooling-Oceans-Triggered-GOBE/tree/v1. 2); this is the recommended mode of access as it will contain any updates and clarifications. Yannick Copin's code used for the Taylor Diagram in Fig. 3  is available on GitHub (https:// gist. github. com/ycopin/3342888).
LIDAR is an atmospheric sounding instrument based on the use of high-power lasers. The use of these lasers involves fulfilling obligations with respect to air safety. In this article, we present a low-cost air traffic surveillance solution integrated into an automated operating system for the Rayleigh-Mie-Raman LIDAR of Clermont Ferrand and the statistical elements of its application over more than two years of operation from September 2019 to March 2022. Air traffic surveillance that includes the possibility of shutting off lasers is required by international regulations because LIDAR is equipped with a class four laser that presents potential dangers to aircraft flying overhead. The original system presented in this article is based on software-defined radio. ADS-B transponder frames are analyzed in real-time, and laser emission is stopped during LIDAR operation when an aircraft is detected within a 2 km radius around the LIDAR.
Overview of the ADS-B System Advances in radio frequency (RF) systems such as software defined radio (SDR) allow surveillance of the airspace around the LIDAR. Furthermore, SDR is a system that favors software signal processing over the hardware usually used in conventional RF systems. An electronic system based on the principle of SDR permanently receives the ADS-B signal emitted by the aircraft on the 1090 MHz frequency. There are different types of ADS-B link available including the 1090ES ("1090 MHz Extended Squitter") that we use. It is an extension of the MODE-S radar transponders, which transmit on 1090 MHz. On airplanes equipped with MODE-S and TCAS (Traffic Collision Avoidance System), these transponders already make it possible to send and receive 56-bit messages. A modification allows them to send 112-bit messages, which is sufficient for ADS-B.
Data Acquisition The data "server" software, DUMP1090, is a software that can be downloaded freely from GitHub  [13]. This software provides the received signal strength and decodes the ADS-B frames to obtain, if available, the identifier of the aircraft, its altitude and its full coordinates (latitude, longitude, speed, direction). We used the DUMP1090 freeware suite from GitHub. It was released under the BSD three-clause license. DUMP1090 was firstly written in 2012 by Salvatore Sanfilippo and then upgraded by Malcolm Robb. DUMP1090 is a simple MODE-S decoder specifically designed for RTL SDR devices. Its main features are robust decoding of weak messages, This dongle is supplied with a mini magnetic antenna but we prefer to use an omnidirectional Discone SD3000 antenna. More generally, our receiving system is driven by a Raspberry Pi 3B, which is able to drive the dongle, process the data and transmit the alerts via an ethernet connection on the local network where the LIDAR computer can monitor them to switch off the laser.
Data Acquisition The data "server" software, DUMP1090, is a software that can be downloaded freely from GitHub  [13]. This software provides the received signal strength and decodes the ADS-B frames to obtain, if available, the identifier of the aircraft, its altitude and its full coordinates (latitude, longitude, speed, direction). We used the DUMP1090 freeware suite from GitHub. It was released under the BSD three-clause license. DUMP1090 was firstly written in 2012 by Salvatore Sanfilippo and then upgraded by Malcolm Robb. DUMP1090 is a simple MODE-S decoder specifically designed for RTL SDR devices. Its main features are robust decoding of weak messages, network support, embedded HTTP, single-bit error correction, the ability to decode the majority of DF messages and to decode raw IQ samples from files, an interactive commandline-interface mode where aircraft currently detected are shown in a list as more data arrives, CPR coordinate decoding and track calculation from velocity, TCP server streaming and raw data sharing capacity between connected clients (using-net).
Analysis and Decision-Making The python 2. 7-based "client" software, "SDRAirSafeLid"  [11] , which was developed at the Technological Development Department of OPGC, uses a function "socket" which uses a low-level networking interface so we can read the data flow from the server and process this stream. An events management protocol has been developed considering the aircraft coordinates, received signal strength and other information such as altitude or knowledge of usual air traffic. This software compares the proximity indicators received with predefined critical thresholds every second. This periodicity is compatible with the implementation of the required air safety. When the position of the aircraft is received, the ground distance separating the aircraft from the laser beam is calculated and compared to a critical distance fixed at 2 km.
ADS-B Alerts Management by COPLid When an aircraft is detected by the ADS-B system, a byte is set to 1 and sent to the LIDAR supervisor software using a TCP/IP socket. As a server, the LIDAR PC receives this byte from the ADS-B system hosted on a Raspberry Pi connected to the LAN of the OPGC. This causes the LIDAR supervisor software to activate an alarm state. In this state, the laser stops emitting any radiation. As long as the byte is set, the supervisor remains in the alarm state. When the ADS-B system resets to 0 for the alarm byte, the LIDAR PC acknowledges it. Then the LIDAR supervisor leaves the alarm state and enters a temporization state for 20 s. This delay is necessary to avoid multiple stops and restarts of the laser caused by an individual ADS-B alert; for instance, when the trajectory of an aircraft cuts the warning perimeter several times or when the alert is generated by the strength of the received signal.
All this means that when an ADS-B alert occurs, we assume that the minimum average time of lost data is actually 50 s of temporization and warmup plus a 30 s average lost record. Therefore, each isolated ADS-B alert provokes an average of 80 s of lost data, plus the duration of the alert itself. An air traffic detection data set was acquired during the two years 2020 and 2021. The security system operated continuously under normal conditions of the LIDAR. Thanks to the log files of ADS-B events continuously recorded since 2020, it has been possible to assess the monthly and daily duration of lost data (Figures  5  and 6 ). The mean duration of ADS-B alerts is 6 h a month and 702 s a day. The average duration per day of ADS-B alerts is spread over an average of 15 eve per day which means 15 warmup times to add (50 s) and 15 data files lost with an avera time of 30 s.
Our system processes the available ADS-B information from nearby aircraft and, if needed, stops laser emission with enough efficiency that the current aviation safety requirements are met. Consequently, in accordance with the evolution of air safety rules, the OPGC site has been successfully accredited by the French air authorities, the "Direction Générale de l'Aviation Civile" (DGAC). Concerning LIDAR data quality control, the rate of missing data caused by air traffic interruption is about 2%. This is a satisfactory result with regard to the data temporal coverage requested by observation networks such as ACTRIS. We are still working to improve our system. For example, this rate has been recently lowered by improving the algorithm so that it considers the specificity of helicopters (local traffic).
Dataset obtained from in silico experiment with the APSIM-wheat Crop model APSIM-Wheat (www. apsim. info) was used to simulate a dataset (https:// doi. org/ 10. 5281/ zenodo. 47296 36; for details, see  Casadebaig et al. 2016)  with 9100 virtual genotypes (N gen = 9100) grown under 9000 environments (N env = 9000). In short, virtual genotypes were created by varying the value of 90 independent physiological parameters in a range of ± 20% from the reference cultivar Hartog, which represents the default parameter values in the APSIM-Wheat. Environments in the dataset contain historical climate data of 125 years  in four locations in Australia (Emerald, Narrabri, Yanco and Merredin, see also Table  1  from  Casadebaig et al. 2016)  in Australia, in combination with two CO 2 levels (380 and 555 ppm), three nitrogen levels (low: 50%, control: 100% and high fertilization: 100% plus 50 kg ha -1 ) and three sowing dates (early, control and late).
Computation of stability indices of the virtual genotypes with three sampling methods All analyses were implemented in the R environment (R Core Team 2020) where 11 stability indices (SI) were calculated in a customized package toolStability  (Wang and Chen 2022 ; https:// github. com/ Illus trati en/ toolS tabil ity). The SI in toolStability include static and dynamic concepts of stability  (Becker and Léon 1988). Under the static concept, the trait of a stable cultivar stays relatively unchanged across different environments. In contrast, dynamic concept takes the environmental mean into account and considers the interactions between genotypes and environments. Furthermore, each concept can be further classified as parametric or nonparametric. In toolStability, there are two parametric SI of static concept: environmental variance  (Römer 1917)  and adjusted coefficient of variation  (Reckling et al.
Using R package toolStability as a tool for reproducible analysis To study yield stability systematically, we developed and shared an R package toolStability to analyse a virtual dataset containing ~ 82 million simulation outputs obtained from the APSIM-Wheat crop model. Our R package toolStability provides more indices in comparison with other published R packages (Branco 2015;  Ajay et al. 2018; Yaseen et al. 2018 ) and online tool platform  (Pour-Aboughadareh et al. 2019). Furthermore, toolStability adds genotypic superiority measure (P i,yield ,  Lin and 1988) , a stability index which was not implemented before. While the characteristic of different SI and their pairwise correlations have been studied and reviewed in the past  (Fasahat 2015; Mohammadi and Amri 2008; Piepho and Lotito 1992) , there is no consensus in favour of a representative SI  (Reckling et al 2021).
Insights from physiological networks regulating stability In APSIM-Wheat crop model, the interactions between physiological parameters, environment and crop management on canopy development (leaf area index, LAI), flowering time, grain yield, grain size and grain number were predicted as a function of physiological assumptions of the model. The simulated dataset provides us a chance to glance at the contour of the complex physiological network and its relation to the shape of phenotype distributions (Fig. 4  and Supplementary Fig. S4 ). Whereas, for the complex trait like grain yield, it is difficult to decipher the genetic and physiological regulations due to pleiotropic effect of genes and the minor contribution of each quantitative trait gene  (Schulthess et al. 2017; Parent et al. 2018).
2014; Asseng et al. 2019 ) and the trade-off between grain number and grain size  (Lichthardt et al. 2020; Voss-Fels et al. 2019) , can be confirmed. Although the high correlation between model outputs (e. g. grain protein content and grain yield) is not always observed in the empirical datasets  (Oury et al. 2003) , a R 2 of 0. 6 has been reported  (Lollato and Edwards 2015; Voss-Fels et al. 2019). Highly positive correlations between the stability of LAI, straw yield and grain number in the r-network (Fig. 6b ) suggested that stable canopy development during the vegetative phase ensures sufficient pre-anthesis nitrogen reserves for grain filling and thereby yield stability. Physiologically, stable and vigorous canopy development ensures radiation interception  (Tian et al. 2011 ) and allows storage of nitrogen and water-soluble carbohydrates in the canopy at the end of the vegetative phase (referred to as pre-anthesis nitrogen and carbon reserves, respectively).
Acknowledgements The authors gratefully acknowledge the technical support by  Dr. Katrin Leinweber  for her help in developing the R package toolStability and setting up with GitHub. We also thank  Magnus Alder  form  Leibniz Universität Hannover  for maintaining the server for simulation. The study was funded by  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)  under project number  419973621 ).
Data availability All data supporting the findings of this study are available within the paper and within its supplementary data published online. An R package toolStability was published on CRAN (https:// cran. r-proje ct. org/ web/ packa ges/ toolS tabil ity/ index. html) and Zenodo (https:// doi. org/ 10. 5281/ zenodo. 58042 12). An APSIM-Wheat dataset is available on Zenodo (https:// doi. org/ 10. 5281/ zenodo. 47296 36). A repository for reproducing the figures in this publication is available on GitHub (https:// github. com/ Illus trati en/ Wang_ 2023_ TAAG) and Zenodo (https:// doi. org/ 10. 5281/ zenodo. 75624 20).
International reactor physics experiment evaluation project (IRPhEP) The OECD-NEA International Reactor Physics Experiment Evaluation Project (IRPhEP)  (Briggs and Gulliford, 2014)  seeks to preserve and evaluate integral reactor physics experiment data to support nuclear energy and technology needs. International contributions are collated within the IRPhEP Handbook (NEA, 2022b). Measurements found in the Handbook include criticality, buckling and extrapolation length, spectral characteristics, reactivity effects, reactivity coefficients, kinetics, reaction rate distributions, power distributions, isotopic compositions, and/or other miscellaneous types of measurements for various types of reactor systems. Distributed with the IRPhEP Handbook and available online is the IRPhEP Database and Analysis Tool (IDAT)  (Hill et al.
International assay data of spent nuclear fuel database (SFCOMPO) SFCOMPO is the largest international database of open experimental assay data for spent nuclear fuel, publicly available online at https://www. oecd-nea. org/jcms/pl_21515/sfcompo-2-0spent-fuel-isotopic-composition. The database is hosted by the NEA and managed by the SFCOMPO Technical Review Group (TRG) under the NEA Nuclear Science Committee -Working Party on Nuclear Criticality Safety (NEA/NSC/WPNCS). Assay data in the SFCOMPO database consists of datasets of measured nuclide concentrations of well characterized irradiated nuclear fuel samples, with description of samples' characteristics and operation histories being provided with adequate detail for potential use in benchmark models. Assay data are provided for 750 samples selected from fuel irradiated in 44 reactors, with 8 reactor types and over 24,000 measurement entries, and cover measurement data for 91 nuclides.
SFCOMPO originates from the database compiled in the 1990s by the Japan Atomic Energy Research Institute (JAERI), which consisted of a series of webpages with tables of measured data for fuel samples from 7 pressurized water reactors (PWRs) and 7 boiling water reactors (BWRs). This database was transferred from JAERI to NEA in 2001, has been hosted by NEA since and has been expanded significantly through the international community's concerted effort led by the NEA Expert Group of Assay Data of Spent Nuclear Fuel (EGADSNF) during 2001-2013. Development of a new, modern functionality database with a standardized format and improved accessibility was initiated in 2013 and culminated with the release in 2016 of the SFCOMPO 2. 0 new graphical user interface  (Michel-Sendis et, al. 2017). This interface enables userfriendly content browsing and data visualization, and easy access to the primary references.
The SFCOMPO TRG has been mandated by the NEA/NSC/ WPNCS to maintain and further coordinate the development of the SFCOMPO database. The TRG's members and contributors are science and engineering experts from academia, industry, and research institutions worldwide. The SFCOMPO TRG's mission is complementary to the efforts of IRPhEP and ICSBEP TRGs, to extend the applications beyond nuclear criticality safety or steadystate in-core analyses and support a broad range of fuel cycle needs, including radiological safety, source terms, shielding, and repository analyses. The primary missions of the SFCOMPO TRG are: 1) preservation of data (preserve and capture legacy data as well as new data as they become available); 2) accessibility of data (ad hoc formatting of the data, and continuous adaptation of the SFCOMPO database and its interface tools to address current and future needs); 3) evaluation of data (perform international peer-reviewed assessments to produce qualified benchmarks suitable for code validation); and 4) knowledge transfer (develop guidance for qualification of data evaluations and train new generations of experts through the evaluation/review process).
Evaluation of data for developing benchmarks is the current focus  (Ilas, et al. , 2020)  of the TRG. Since 2019, two evaluations have been approved for release and are pending publication ("Evaluation of Three Mile Island Unit 1 Fuel Samples-Assemblies NJ05YU and NJ070G (Type 15 × 15)" by Georgeta Radulescu, and "Evaluation of Fukushima-Daini-1 Samples-Assemblies 2F1ZN2 and 2F1ZN3 (Type 9×9-9)" by Ugur Mertyurek). A further set of three draft evaluations are pending finalization of the independent review and approval by the TRG. To address community's great interest in decay heat data addition as a new key spent fuel metric to the SFCOMPO database, a task force was established in January 2022 to prepare, review, and curate full-assembly decay heat experimental data for addition as new entry datasets in the SFCOMPO database.
The requirements that are being developed for decay heat addition will be consolidated with previous enhancements of the database and its interface. SFCOMPO is an invaluable asset serving the needs of the international community. Validity of safety assessments for handling irradiated nuclear fuel including transportation, storage, processing and recycling, and repository applications is largely based on capabilities to accurately predict the evolution of nuclides during and after irradiation in fuel and structural materials. Experimental assay data are essential for evaluating bias and uncertainties in spent nuclear fuel safety analyses and provide one means for determining uncertainties in integral quantities important to safety, such as decay heat or spent fuel reactivity, and to validate nuclear data.
The importance of experimental assay data for code and associated nuclear data validation goes well beyond the back end of the fuel cycle applications, to impact any area where accurate estimation of nuclide inventories is impactful. Most of the experimental data in SFCOMPO applies to light-water reactor fuel. However, data are available from previous MAGNOX and advanced gas-cooled reactor (AGR) fuel experiments and the database can be easily expanded to include any advanced reactor fuel assay data once they would become available. The current database and the evaluations being developed provide great support in assessing the abilities of the underlying methods and nuclear data for advanced reactors of accurately predicting the nuclear transmutation and decay physics.