dataesr/scientific-paragraphs-categorization huggingface dataset:
The average ecosystem infrastructure appeared relatively stable over the 12-year period, for two reasons. First, the network of protected areas combined with limited access conditions has contributed to maintaining the ecological potential. Second, degradation was observed when the analysis was performed by DLCTs, for wetlands in particular, along with water bodies, forests and pastures (data not shown). In general, the picture underestimates the watershed's actual resource base condition due to limitation in available data and information. The gaps for bio-carbon health index and the ecological unit value for ecological infrastructure demonstrate this.
Ekins et al. , 2019)020) that the best option would be the systematic assessment of the state and dynamics of the ecosystem capital covering its core components, namely land use, and the state of water/rivers, biomass and ecosystem infrastructure. This is what ENCA does, with the aim of engaging various stakeholders and the society at large in the evaluation and the stewardship of their territory. The systematic integration of the required variables in the ENCA framework, their spatial breakdown, cross-checking and update brings more than the possibility of calculating particular indicators such as total ecosystem capability and its degradation. The ENCA database is at the same time a possible source of preprocessed data for a range of other applications. As such, it would provide linkages to the variety of applications or projects.
Acknowledgements We wish to thank  Charlotte Weil  for her critical reading of the manuscript and productive suggestions of improvement. We are grateful to the two reviewers for their patience and precious advice in making the article accessible to all. Financial support. This work was supported by the  National Council on Science and Technology, Mexico (CONACyT)  ( Argüello Velazquez Jazmin Adriana , grant number  225397/410110 ), and the  Ecole Normale Superiéure de Lyon, France : the  Laboratory of Plant Reproduction and Development , the  Michel Serres Institute , and the  Rhône-Alpes Institute of Complex Systems.
The statistics were downloaded from AGRESTE (2012). 2. Wood removals. The vector database of forested area (IGN,1992-2004) served for the spatialisation of the wood removal statistics (except energy) for 2000 (source Weber, 2018), 2006, and 2012 (AGRESTE, 2005- 2017). 3. Withdrawals of animal bio-carbon. The cattle statis- tics downloaded from AGRESTE (2000-2015) and spa- tialised in the pasture class years 2000, 2006, and 2012 from CORINE (2017). Intensity of use Net ecosystem production (corresponding to net The intensity of carbon use index is the ratio of ecosystem accessible carbon) divided by use. Net accessible resource surplus to Total use of ecosystem bio-carbon. Values greater than one were set to one. health index Indicators of ecosystem health regarding bio-carbon are changes such as the mean age of forest or fish stocks, and vulnerability to fire.
An additional health indicator is the dependency of bio-carbon production on fossil energy inputs. 4. Loss of bio-carbon due to land-use change. The number of hectares and the type of change of land use were computed from the land cover CORINE (2017) dataset for changes from 2000 to 2006. The sum of the corresponding type stocks was multiplied by the number of hectares changed. This period was assumed as the median and used for the other two periods (namely 2006 and 2012).
In italic font, the compounds highlighted as significantly affecting the variable and compounds in bold font: highest regression coefficient (results of PLS analyses). Variables in bold font: significant results between fire modalities. PHF: Pinus halepensis "Fire" modality. PHNF: Pinus halepensis "No-Fire" modality. The PCAs performed on both species' datasets indicated that the three first components explained more than 67% of the total variance (69. 79% for P. halepensis and 40. 01% for P. sylvestris, Figures  1b  and 2b ) with eigen values higher than one. For both species, the flame height was positively related to the first component as was the rate of spread for P. halepensis. For this species, the time-to-ignition variable characterized the second component and the consumed weight variable characterized the third one.
Area and Species Studied Southeastern France is the region the most affected by fires (with 4657 fires and more than 21,000 ha burned during the 2010-2020 period according to the regional fire database Prométhée; available online: www. promethee. com (accessed on 8 October 2021)). Generally, this region is considered to have a typical Mediterranean climate with mild and humid winters, hot and dry summers, and calcareous soils (i. e. , limestone). However, Mediterranean areas also have a high diversity of sub-climate, vegetation, and landscape characteristics, inducing different fire regimes on a finer scale  [9]. In order to account for this variability, sampling sites were located on a North-South gradient in the Provence region (in southeastern France, Figure  4 ), which corresponded to a gradient from the meso-to sub-Mediterranean climates  [65]  and, therefore, to a gradient in fire regimes.
Sampling Plan Even if the Mediterranean basin is prone to recurrent fire events, knowing the exact fire history of a location over the last century is sometimes difficult, especially when many species do not present with fire scars. In Southeastern France, the exhaustive monitoring of fire events was established approximately 60 years ago, providing reliable information on fire frequency, but not on fire intensity, for this period. In this study, the recent fire history (between 1959 or 1973, depending on the area, and 2018) was reconstructed using different regional fire databases (the fire database of Office National des Forêts/Direction Départementale des Territoires et de la Mer, has been georeferencing fire perimeters since 1961, and the Prométhée fire database has been recording fires since 1973, at the earlier) as well as satellite image analyses to refine fire perimeters (using the open-source QGIS software; https://www.
qgis. org/, accessed on 8 October 2021). These databases were used for sampling tree populations according to the fire modality. For both species, half of the populations were sampled in unburned areas ("No-Fire" modality, i. e. , no fire in the last 47-59 years, depending on the database) and the other half in areas that had been subjected to at least one fire ("Fire" modality; i. e. , one fire or more in the last 47-59 years, depending on the database). All of P. halepensis' "Fire" populations were sampled in areas having experienced stand-replacing fires, thereby having completely burned during these past events. In contrast, in P. sylvestris' "Fire" populations, some trees resisted the past fires, inducing a post-fire recruitment based solely on tree survival (only trees younger than the age of the last fire were sampled in order to account for trees that grew after the fire event).
Samples (N = 1140 for P. halepensis and N = 900 for P. sylvestris) were burned using a 4 kW radiant panel (switched on 30 min before burning tests in order to have a steady heat release) as an ignition source (Figure  5 ) due to its stability in terms of heat release compared to other types of ignition sources  [77]. The radiant panel was placed horizontally, allowing the heat to radiate upward, while the shoot sample was placed 1 cm above the center of the radiant-panel grid. Three 0. 25 mm type K (Chromel-Alumel) thermocouples, two located 5 cm above the radiant-panel grid and one 10 cm above the center, were linked to a data logger (ALEMO 2590 Ahlborn, Ahlborn Mess-und Regelungstechnik GmbH, Holzkirchen, Germany) to record the temperature variation (one record per second) during the burnings to assess the combustibility component.
P. halepensis For each tree, three burnings of 20 g were performed (in total, 1140 burnings) in a 27 cm diameter circular aluminum tray (Figure  6 ). In addition, the sample thickness and weight had to be homogeneous among burnings (as it corresponded to the height of the aluminum tray) because the bulk density is an important factor inducing flammability variation  [78]. Trays were disposed on an electronic scale in order to measure the sample weight loss during the burning and to calculate the consumed fuel weight. The temperature emitted by the flames was measured with four type K 0. 25 mm (Chromel-Alumel) thermocouples linked to a data logger and placed according to the four cardinal points, five centimeters above the litter sample and at equal distance between the center and the edge of the tray (i.
e. , 13. 5 cm from the center). The data logger measured the temperature every second, and the data were averaged during post-treatment. The ignition source was a standard (i. e. , calibrated) flaming pine wood piece (2 cm × 2 cm × 1 cm) called a "firebrand" that was placed in the center of the sample  [78]. This method ensured an identical ignition for all of the samples. The "firebrand" was ignited using a 500 W epiradiator and, after ten seconds of the flaming phase, it was dropped off in the center of the litter sample. Once touching the litter, the chronometer was switched on to record the time-to-ignition (TTi) and the flaming duration (FD). After the litter ignition, the pine cube was removed from the sample.
Flammability Cofactors In order to better link terpenes and flammability according to the fire modality, common driving factors used in flammability studies (see data analysis) were considered for the statistical analyses. Even if shoot conditioning was performed to reduce the FMC range (90. 5 ± 8. 9% for P. halepensis and 103. 2 ± 10. 5% for P. sylvestris) among the populations and the trees, this factor could still have an impact on flammability and had to be considered in the analyses to avoid any possible overriding effect on terpene content. In addition to the FMC, two other traits (litter needle surface-to-volume ratio and shoot bulk density) possibly driving flammability were also measured for the statistical analyses. Along with the packing ratio, the bulk density is an important factor to be considered in a flammability assessment  [22, 78].
One mL of solution was injected at 250 • C into a capillary column (HP-5MS, Agilent J&W GC Columns, Agilent Technologies, Montpellier, France) at a constant flow (i. e. , 1 mL min -1 , helium as the carrier gas). At the beginning of the process, the temperature was 40 • C and increased at a rate of 3 • C min -1 up to 300 • C during the analysis. A solvent delay of 5 min was observed, and the total run time was 70 min. The terpene molecule identification was carried out using MassHunter spectrometry software. The identification was based on the molecule retention time (RT) and on their mass spectrum. All of these identifications were checked by comparing their retention index (RI) to common libraries  [82]. The retention index was calculated using the alkanes injected at each session (3): RI(mol) = 100 × X(a) + (RT(mol) -RT(α))/(RT(β) -RT(α)) × 100 (3) where α is alkane before the molecule, β is alkane after the molecule, X is carbon number, and RT is retention time.
5, 0. 25 and 0. 125 Hz, respectively). Complete ferroelectric switching was not achieved as sample breakdown occurred at voltages exceeding 4 kV (electric field strength 4000 V mm À1 ). We emphasize that this applied field is insufficient to achieve true switching in the sample; instead we observe the response of the individual domains to the applied field, which is indicative of the softest switching pathways. Detector acquisition was triggered simultaneously with the initiation of the voltage such that 100 X-ray diffraction patterns were recorded, evenly spaced across the entire voltage cycle. The 80 s voltage cycles were additionally performed with a capture rate of 300 diffraction patterns over the time period, yielding a data set with a greater time reso-lution but reduced intensities.
As with the one-step mechanism, both of these routes may occur via the X þ 2 or the X À 3 mode. To simulate the effect of rotation, tilt and polar modes upon the intensity of the reflections, distortions of the theoretical I4/mmm structure were calculated using the ISODISTORT program  (Campbell et al. , 2006). By choosing different OP directions for the X þ 2 and X À 3 irreducible representations ('irreps'), we generate isotropy subgroups of I4/mmm of various symmetries. For example, when the OPs are in the directions (0; a|0; b), the I4/mmm structure is transformed to A2 1 am [basis = (001), (  110 ), (110), origin shift = (1/4, 1/4, À1/2)]. When both OPs are along general directions (a; b|c; d), the space group resulting from the distortions is Pm [basis = (110), (  110 ), (001), origin shift = (0, 0, 0)].
By incorporating this lowered symmetry into a symmetry-mode model in the Rietveld refinement program TOPAS Academic v6, it is possible to individually vary the magnitudes and directions of the distortions and plot the effects on the intensity of each reflection. Starting with both modes fixed at values refined in a previous publication (defined as switching coordinate 0) and manipulating their amplitudes both individually and simultaneously  (Pomiro et al. , 2020) , we mimic the effect of unwinding the distortion modes (complete unwinding being switching coordinate 0. 5), moving through different switching mechanisms to a complete reversal of the distortions (defined as switching coordinate 1). shortest switching coordinate, changing by 10% around a switching coordinate of approximately 0.
Data Sharing The extent to which experiments share data will be key to how much we can learn from the next galactic core-collapse supernova. In the current version of SNEWS, experiments send a packet stating that they have seen something consistent with a burst of supernova neutrinos at some time. If multiple experiments see a burst within 10 s, an alert will be issued (Sec. 1. 1). More information exchanged would make the most of this rare opportunity. For example, individual experiments' neutrino "light curves" may provide the quickest, albeit rough, triangulation (Sec. 3. 2) which can help prepare telescopes set up for electromagnetic observations. Features evident only in the combined light curves may also influence follow-up strategy (see Sec. 5. 3), and a precise neutrino arrival time can help define a gravitational wave search in the case of spherical symmetry producing smaller waves (Sec.
5. 2). Extensive data sharing may be even more important for the detection of low-flux events such as SNIa, pair instability SNe, and pre-supernova neutrinos, which may yield only marginally significant signals in any individual experiment. If experiments share rolling updates of their "pre-supernova significance", their combination would increase the sensitive range and advance warning time for the explosion, and indeed provide important information on the dynamics within the supernova progenitor (see  Sec. 1. 3). Participating experiments will choose the degree of data sharing that they are most comfortable with. Functional levels of data sharing for a given detector can be divided into three example tiers: (i) Alert Tier: The detector sends a message to SNEWS 2. 0 indicating that it sees above-threshold activity.
The drawback is that such derivations may take considerable time, introducing a latency which could limit its usefulness. Timing Tier sharing, on the other hand, is intended to gather information which is available with low latency and from which a rough pointing can be derived; this could help telescopes prepare their observation strategies, which can then be refined with further data (some of which may fall under Significance Tier sharing). As mentioned earlier, Timing Tier data sharing may also reveal features which would be important in multi-messenger follow-up. All the data shared with SNEWS remains the property of the originating experiment, and each experiment will have its own connection with the SNEWS server which will be private from all other users. SNEWS will store different categories of shared data for the following periods of time: one month, for all Alert Tier data, and overall p-values under the Significance Tier, in order to catch irregularities; 48 hours, for pre-supernova data under Significance and Timing Tiers; and one hour, for supernova data under Significance and Timing Tiers.
Walkthrough Example While the details of the implementation are still being developed, a block diagram of the flow of an improved SNEWS 2. 0 network is shown in Fig. 11. Each experiment can contribute to one or more data streams, e. g. , a time series of the significance of a supernova neutrino signal over time in that experiment, or a traditional alert to be used to form a coincidence, as in the original SNEWS. In the event of an alert, extra information to be used in triangulation could be sent, or neutrino "lightcurves", distance estimates, or other useful items. The server itself will sort, collate, and buffer the incoming information, making it available to analysis plugins that operate on that data. One plugin will combine significance time series to search for signals that might be sub-threshold in any given experiment.
This reduces the expected background rate below 1 Hz per detection unit. With this selection, the signal expectation for the accretion phase of a CCSN exploding at 10 kpc is between 50 and 250 events for a 115-lines instrumented block. This selection is used for the sensitivity estimation and implemented in the real-time KM3NeT supernova trigger. The KM3NeT detectors will be sensitive beyond the Galactic Centre, reaching the coverage of ∼90-95% of progenitors in the Galaxy. Both the online and offline searches are applied within a time window of 500 ms, covering the accretion phase. For the realtime trigger, data is sampled with a frequency of 10 Hz. The expected detection horizon for a false alert rate compatible with the SNEWS requirement varies between 13 and 26 kpc, depending on the considered flux model.
For the purpose of CCSN astrophysics studies, the signal statistics can be improved using a selection of all coincidences (two or more hit PMTs on a DOM) at the cost of a lower purity. With a complete detector at each site (one instrumented block in ORCA and two in ARCA), KM3NeT is expected to collect 20-50,000 events (see Table  4 ). It will be able to study the neutrino light-curve for signatures of time-dependent patterns, the determination of the arrival time of the burst and the characterisation of the neutrino spectrum (M. Colomer et al. , 2019). The effective mass of a KM3NeT 115-lines instrumented block is 40-70 kt for all coincidences and 1-3 kt for coincidences with six or more hit PMTs. The KM3NeT real-time supernova search is currently operational with a dedicated data processing pipeline, running on the already connected detection units.
Alerts can be sent within 20 s from the generation of the corresponding data off-shore. The triggering capability under SNEWS requirements already reaches sensitivity to progenitors closer than 4. 5-8. 5 kpc. This horizon will increase in the near future with the deployment of new detection lines. 6. 2. Scintillator Detectors 6. 2. 1. Baksan The Baksan Underground Scintillation Telescope was one of the detectors which observed neutrinos from SN1987A  (Alekseev et al. , 1987)  and is still operational, having continuously watched for SN neutrinos from 1980 till the present  (Novoseltsev et al. , 2020). The experiment is at a depth of 850 mwe in the Baksan Valley, Kabardino-Balkari, Russia. The experiment's 330 t of liquid scintillator is primarily sensitive to positrons produced by IBD, and is divided into two independent sub-detectors operating in coincidence with each other.
The absolute errors of the reconstructed coordinates x, y, z for a point-like event in the target are virtually the same and depend on the energy as ∼10 cm/ E (MeV). To classify the events, pulse shape discrimination methods (PSD) are actively applied. Since the main DAQ of the Borexino detector ("LABEN") was designed for spectroscopy of solar neutrinos with energies in the sub-MeV energy range, a special additional data acquisition system based on flash ADCs ("FADC") was designed to record neutrinos with energies up to ∼50 MeV from supernovae. Both systems are working and will continue to function until the end of the experiment. Borexino joined the SNEWS community in 2009. Originally there were two independent modules for the LABEN data analysis and generation the alarms: the Echidna Online supernova monitor and the Princeton supernova monitor.
Currently only the latter is operational. The Borexino experiment is approaching its end. The data taking is planned to stop in 2021. 6. 2. 4. KamLAND Kamioka Liquid Scintillator Anti-Neutrino Detector (KamLAND) is an experiment to detect νe signals using 1000 t of ultra pure liquid scintillator. The KamLAND detector occupies the former site of the Kamiokande experiment in the Kamioka mine (2700 mwe overburden). A spherical balloon of 13. 0 m diameter is suspended in the center of the KamLAND detector and filled with 1000 t ((1171 ± 25) m 3 ) of ultra pure LS. Scintillation photons are detected by 1,325 fast 17-inch aperture Hamamatsu PMT custom-designed for KamLAND, and 554 20-inch PMTs inherited from Kamiokande. The PMT array are attached inside on the spherical stainless steel tank of 18.
The trigger readout window is roughly 400ns and is essentially deadtime-less. SNO+ began taking data with an AV filled with water in May 2017. Over the course of 2020, SNO+ is replacing the water in the AV with 780 t of scintillator, and soon after expects to load the detector with 0. 5% (by mass) of tellurium, resulting in 1330kg of the target isotope. Techniques for increasing the Te load are promising. As with other liquid scintillator experiments, SNO+ will be sensitive to IBDs and neutrino elastic scatters off electrons. The high IBD efficiency, combined with a very low background rate, will be exploited to add to the SNEWS pre-supernova alert. In addition, the low background rate makes it possible to observe elastic scattering off protons. Though the energy deposit from such scatters is highly quenched, they may represent nearly half of the neutrinos observed from a supernova at SNO+, and are an important channel for flavor-agnostic neutral current interactions.
The NOvA DAQ employs a buffered readout system that allows for continuous readout of the detector. In its current configuration, the DAQ writes 45 s of continuous data to disk in the event of a supernova trigger. NOvA subscribes to SNEWS and issues a supernova trigger for any SNEWS alert. A data-driven supernova trigger has also been developed and running on both detectors since November 2017  (Acero et al. , 2020). This data-driven trigger removes known backgrounds and groups hits into candidate IBD interactions. A supernova will manifest as a sudden increase in the rate of IBD candidates, which then slowly returns back to baseline. A time series of IBD candidates is constructed in real time and compared against two hypotheses, one which represents the background only and another which represents a signal superposed on the background.