dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Availability Statement: Not applicable. Conflicts of Interest: Potential competing interests. A. K. : Co-founder and shareholder of A. J. M. Medicaps; co-director and shareholder of iCERV Ltd. ; consultancy fees (Jinshan Ltd. ); travel support (Jinshan, Aquilant, and Dr Falk Pharma); research support (grant) from ESGE/Given Imaging Ltd. and (material) IntroMedic/SynMed; honoraria (Dr Falk Pharma UK, Ferring, Jinshan, Medtronic). Member of Advisory board meetings (Dr Falk Pharma UK, Tillots, ANKON). A. H. : Co-founder and shareholder of Augmented Endoscopy.
Firstly, we create a set of plausible model representations of the Antarctic Ice Sheet with the Parallel Ice Sheet Model (PISM). They are forced from 1850 to 2015 by historic changes in the ocean and atmosphere from a simulation of the Coupled Model Intercomparison Project Phase 5 to ensure that they replicate the present-day trend in mass loss. Sub-shelf melt rates are calculated using the Potsdam Ice shelf Cavity mOdel (PICO;  Reese et al. , 2018a). Observed ongoing retreat in the ASE sector is linked to oceanic forcing  (Jenkins et al. , 2018)  and recent projections underline the importance of the sensitivity of sub-shelf melting to ocean temperature variations  (Jourdain et al. , 2020; Seroussi et al. , 2020; Reese et al. , 2020). We thus calibrate the sub-shelf melt module PICO to represent observed  (Jenkins et al.
Experimental design and initialisation The primary aim of this manuscript is to analyse the long-term evolution of Antarctic grounding lines under current climate conditions. To do this, we first require initial configurations that represent the Antarctic Ice Sheet under present-day climate conditions. We then run these initial states forward in time, and let them evolve under constant atmospheric and ocean conditions. The strategy adopted to build initial present-day ice sheet configurations with PISM relies on spin-up. We test for uncertain- to observations. For all ensemble members, we run historic simulations from 1850 to 2015 with the aim to reproduce current changes in ice sheet thickness. To represent atmospheric and oceanic changes between 1850 to 2014 we use the historic forcing suggested by the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6,  Barthel et al.
In this manuscript we aim to understand the current state of Antarctic grounding lines by analysing their underlying steady states and the reversibility of large-scale transitions if they occur. Since a change in sub-shelf melt rates is thought to be a major trigger for grounding line retreat in Antarctica, we present a new parameter optimisation approach for the melt rate parameterisation PICO. This approach ensures that the sensitivity of melt rates to ocean temperature changes is in accordance with observations or high-resolution ocean simulations. This makes the parameters also suitable for sea-level projections. Using an ensemble of numerical simulations with PISM, we analyse the evolution of Antarctic grounding lines under presentday climate conditions. We find that for some possible representations in our ensemble, current climate conditions can force a retreat of Thwaites Glacier in the ASE sector which eventually results in an irreversible collapse.
A1. Then, we fit a function to the modelled data of thermal driving, salinity and melt rates using least-squares. We expect the melt rates to also depend on salinity because the salinity gradient between the continental shelf and the cavity controls the strength of circulation, which in turn affects the melt rate. We assume that the (time-shifted) melt depends quadratically on thermal driving (as discussed in Section 3. 2) and linearly on salinity. Note that we assume a linear dependency on salinity as we are not aware of a more specific functional relationship. The R-squared value of the fit is 0. 937 and the P-values for the coefficients are smaller than 0. 0005, so we consider this sufficiently good. The resulting, predicted melt rates show a similar pattern of increase as the modelled melt rates, see right panel of Fig.
For the best estimate of FRIS, we use the mean of the FRIS 4xCO2 simulation which is 0. 53 • C. This makes the baseline thermal driving consistent with the fitted sensitivity curve. It is warmer than the average from the data in  Schmidtko et al. (2014) , so yields a slightly larger sensitivity, which is however still smaller than sensitivities estimated from other studies, see Sect. line positions, and a special focus is given to the Amundsen, Ross and Weddell seas. Initial ensemble members were obtained from equilibrium simulations of a full parameter ensemble with the best five runs for the entire AIS, the Amundsen, Weddell and Ross seas continued after 5000 to full 25,000 years (total of 16 runs). From these 9 runs were selected and for each a historic simulation was run from 1850 to 2015.
Description of data collection 1. Data Description There are two folders in the shared public repository, 'DATA_SUCTION' and 'PYTHON_SOURCES. ' The first contains primary and secondary data with a singular 'README' text file. This text file provides additional details about the 30-measurement series, such as experiment time slots and data quality, based on a qualitative evaluation of anisotropy distribution stability. While in the second folder, one would find Python codes used to generate secondary data by treating the primary ones. The primary data, video files with '. avi' format, are retrieved for each recorded ring suction test from the commercial software CS100 supplied with the CutiScan®. Figure  1  represents a snapshot from a video recording, where the dark zone corresponds to the observed skin area.
Hence, a daily data series lasts around 1 hour. As the role of the primary cycle is stabilizing the UV lightening inside the probe, only the measurement cycle is recorded by µEye® camera (Figure  6 ). The 6-seconds video file is then retrieved from CS100, a 'black box' software provided with the device. An alternative DIC program based on the PyDIC suite  [1]  and compatible with data formats has been implemented by our team. PyDIC suite use Lucas-Kanade method optical flow algorithms  [5]  implemented in OpenCV (Computer Vision) Python-library  [6]. The relative correlation parameters: correlation grid resolution and correlation window size are, respectively, 20 pixels and 72 pixels. Those parameters, fixed on the basis of error optimization study, can be easily modified in the algorithm.
>> python3 multi_test. py Several plots can be derived from the secondary dataset. As an example in Figure  7  for a given series and pressure set-point (files path: '2020_12_14\p_300\TABLES), the radial component of the displacement  ̃ evaluated by linear interpolation on the five yellow circles with radius  = {0. 5; 1; 1. 5; 2; 2. 5} mm has been plotted with respect to the angle  ̃. Also, the evolution of the displacement norm ‖ ̃‖ over time has been represented for different angles  ̃= {0°, 45°, 90°, 135°} along the circle  = 1 mm. The Python codes used to generate the graphs are available in https://dmarc. femto-st. fr/fichiers/691. -07 -6,052E-04 3,027E-04 5,280E+02 -07 6,605E-04 -3,301E-04 -07 7,916E-04 -3,957E-04 5,126E+02 2,698E+00 1,594E+02 -3,111E+00 5,122E+00 2,649E-07 -7,281E-04 3,642E-04 5,060E+02 2,663E+00 1,616E+02 -3,080E+00 4,987E+00 1,867E-07 -6,113E-04 3,057E-04 5,000E+02 2,632E+00 1,637E+02.
Despite the close relationship between planetary science and plasma physics, few advanced numerical tools allow bridging the two topics. The code Menura proposes a breakthrough towards the self-consistent modelling of these overlapping fields, in a novel two-step approach allowing for the global simulation of the interaction between a fully turbulent solar wind and various bodies of the solar system. This article introduces the new code and its two-step global algorithm, illustrated by a first example: the interaction between a turbulent solar wind and a comet. -E z, i+2, j -2 + 8E z, i+2, j -1 -8E z, i+2, j +1 + E z, i+2, j +2 -E z.
Menura, the new code presented in this publication, splits the numerical modelling of the interaction into two steps. Step 1 is a decaying turbulence simulation, in which electromagnetic energies initially injected at the large spatial scales of the simulation box cascades towards smaller scales. Step 2 uses the output of Step 1 to introduce an obstacle moving through this turbulent solar wind. The code is written in C++ and uses CUDA APIs for running its solver exclusively on multiple graphics processing units (GPUs) in parallel. Section 2 introduces the solver, which is tested against classical plasma phenomena in Sect. 3. Sections 4 and 5 tackle the first and second step of the new numerical modelling approach, illustrating the decaying turbulence phase, and introducing the algorithm for combining the output of Step 1 together with the modelling of an obstacle (Step 2).
We use a threshold value equal to a few percent of the background density, 5 % in the following examples, threshold below which a node is considered as a vacuum node, and only the resistive terms of the generalised Ohm's law of Eq. (4) are solved using a higher value of resistivity η h vacuum  (Holmström, 2013). This way, terms proportional to 1/n do not exhibit nonphysical values where the density may get locally very low, due to the thermal noise of the PIC macroparticle discretisation. All variables in the code are normalised using the background magnetic field amplitude B 0 and the background plasma density n 0. All variables are then expressed in terms of either these two background values, or equivalently in terms of the proton gyrofrequency ω ci0 and the Alfvén velocity v A0. We define normalised variables ã as obtained by dividing its physical value by its "background" value: ã = a a 0.
Physical tests In this section, the code is tested against well-known, collisionless plasma processes, and their solutions are given by the linear full kinetic solver WHAMP  (Rönnmark, 1982). A polytropic index of 1 is used here, with no resistivity. We first explore MHD scales, simulating Alfvénic and magnetosonic modes. We use a 2-dimensional spatial domain with Table  1. Background values used to normalise all variables in the solver (cf. Eq. 7). B 0 B 0 n 0 n 0 v 0 v A0 = B 0 / √ µ 0 m i n 0 ω 0 ω ci0 = eB 0 /m i x 0 d i0 = v A0 /ω ci0 t 0 1/ω ci0 E 0 v A0 • B 0 p 0 B 2 0 /(2µ 0 ) m 0 m i n 0 x 3 0 q 0 e n 0 x 3 0 one preferential dimension chosen as x. A sum of six cosine modes in the component of the magnetic field along the x direction are initialised, corresponding to the first six harmonics of this periodic box.
The amplitude of these modes is 0. 05 times the background magnetic field B 0 , which is taken either along (Alfvén mode) or across (magnetosonic mode) the propagation direction x. Data are recorded along time and along the main spatial dimension x (saving one cut, given by one single index along the y direction), resulting in the 2D field B(x, t). The 2-dimensional Fourier transform of this field is given in Fig. 2  (Alfvénic fluctuations to the left, magnetosonic to the right). On this (ω, k) plane, each mode can be identified as a point of higher power, six points for six initial modes. The solutions given by WHAMP for the same plasma parameters are shown by the solid lines, and a perfect match is found between the two models. Close to the ion scale k •d i0 = 1, WHAMP and Menura display two different branches that originate from the Alfvén mode, splitting for higher frequencies into the whistler and the ion cyclotron branches.
Scalability An important part of performance testing for parallelised codes is the scalability of the parallelisation. When each parallel process is serial (i. e. one thread for one process), the strong and weak scaling of the code are classical performance tests, with theoretical laws available for comparison (respectively Amdahl's law and Gustafson's law). These laws cannot be directly adapted to the case of devices that already have a highly parallel structure, such as GPUs. We however can approach the same strong and weak scaling properties of the code to get some valuable insights on the performance of the Menura's MPI implementation. Strong scaling refers to the speed-up (gain of computation time) obtained while simulating the same problem (same grid size and same number of particles per grid node) with a growing number of processes (the load thus decreasing on each GPU).
The upper panel of Fig. 7  shows the speed-up obtained using from 1 to 25 processes (i. e. GPUs) solving the same problem: a homogeneous, fully periodic, 2D plasma box, with no initial disturbance, with total size 1000 × 1000 grid nodes and 1000 particles per grid node. V100 cards with 16 GB of RAM were used for all runs but one (see below). The speed-up is measured as s N = t ref /t N. t denotes a computation time, counted from the start of the main algorithm loop to its end, thus excluding the variables' initialisation and output. Each run is completed five times, and the average value for each type of run is given in Fig. 7. t ref is a reference computation time; t N is the computation time using N processes. The results are given in a log-log representation, to emphasise the behaviour of the code at low and large number of processes.
In turn, when an operation needs more threads than available, the computation time is increased. On the opposite end of the test, for N > 16, it is the irreducible operations, such as MPI communication or the kernels calls by the CPU, which become greater than the actual calculation time by the GPUs, and lead the speed-ups to diverge from the ideal linear evolution. The weak scaling of the code is measured by increasing the size of the problem while increasing the number of processes, keeping the same load on single processes. The results are given using the same definition of the speed-up, still using the run N = 4 for reference. Each process now simulates a 1000 × 125 nodes domain with 1000 particles per node (thus corresponding to the previous N = 8 run). Because of the much smaller differences in computation time, the scales are kept linear.
From four to five (and more) processes, communication is now done between two or more computation nodes (with one compute node hosting four GPUs in Jean Zay cluster used here). Communicating data between cards within a same node is faster than between cards on different nodes, and therefore an observable slow-down happens at N = 5. Increasing the number of nodes (from 8 cards to 16, then to 20) does not affect the computation time, for the same reason that increasing the number of cards within one node results in the same computation time. These two tests exhibit a fine behaviour of Menura's MPI parallelisation, also showing that for this problem size, MPI communication does not cost more than 4 % to 8 % additional computation time, depending on the number of nodes used.
Acknowledgements. Etienne Behar  thanks  Matthew Kunz  for the valuable discussion concerning constrained transport in the context of hybrid PIC codes. Financial support. Etienne Behar  received support from the  Swedish National Research Council  (grant no. 2019-06289 ). This research was conducted using computational resources provided by the  Swedish National Infrastructure for Computing (SNIC)  (project nos. SNIC 2020/5-290  and  SNIC 2021/22-24 ) at the  High Performance Computing Center North  ( HPC2N ),  Umeå University, Sweden. This work was granted access to the  HPC resources of IDRIS  under the allocation 2021-AP010412309 made by  GENCI. Work at  LPC2E  and Lagrange was partly funded by  CNES. Shahab Fatemi  received support from the  Swedish Research Council (VR)  (grant no. 2018-03454  and  SNSA  grant no.
Code availability. The code is open-source and available at https: //gitlab. com/etienne. behar/menura (also available on the Zenodo repository  Behar, 2022) , its documentation available at https:// menura. readthedocs. io/en/latest/ (last access: 3 May 2022). Data availability. All data in the publication were produced and can be reproduced using Menura and its analysis tools. Jupyter notebooks are also provided (https://menura. readthedocs. io/en/latest/ physical_tests. html, last access: 3 May 2022) as an example to reproduce the physical test analysis. The data set is not made available online.
, 2020) , and the lack of a sufficient understanding of the economic costs of biological invasions have likely contributed to inadequate spending on IAS management and inefficient decision-making  (Diagne et al. , 2020a (Diagne et al. , , 2021a)). Recently, the InvaCost database has been compiled to fill this gap in collating and understanding reported economic costs of biological invasions in a globally standardised manner  (Diagne et al. , 2020b) , allowing for relevant comparisons across different scales and contexts. Descriptors in this database include a categorisation of the type of cost incurred, distinguishing primarily between damage (i. e. resource degradation or loss) and management (i. e. expenditures to prevent, control or eradicate IAS); as well as the type and stage of management investment (i.
e. pre-invasion, postinvasion, funding/knowledge). National and international studies based on these data have consistently shown that management expenditures represent only a small fraction of the costs of IAS damage (e. g. Bradshaw et al. , 2021; Haubrock et al. , 2021a; Heringer et al. , 2021; Kirichenko et al. , 2021; Kourantidou et al. , 2021; Cuthbert et al. , 2022b). At the same time, overall damage costs have increased at a higher rate than management expenditures  (Diagne et al. , 2021a) , suggesting that the way in which management investments are undertaken deserves careful consideration across time, space and taxonomic groups. The fundamental fact that damage costs are rising rapidly indicates that better management is needed to reduce future impacts. Nevertheless, no studies have placed global management spending in the context of damage costs using the latest version of the InvaCost database or considering different management types.
Such a quantification could provide compelling evidence that management is warranted if management spending is substantially lower than incurred damage costs, and is accordingly not implemented at the sole benefit of agencies involved  (Leung et al. , 2002; Cuthbert et al. , 2020). Here, we use this InvaCost database to identify patterns and trends, research biases, and knowledge gaps surrounding management expenditures against biological invasions worldwide. Our aims were to (i) quantify management costs relative to IAS damage costs, (ii) characterise management expenditures for IAS at different spatial, taxonomic, environmental and temporal scales, (iii) model ecological and socio-economic determinants of damage costs and management spending, as well as potential savings should management have been more timely, and (iv) identify avenues for further efficient spending and research on IAS management.
Data collection and processing We used cost data collected from studies across the globe and presented in the latest version of the InvaCost database at the time of writing (version 4. 0; 13,123 cost entries;  Diagne et al. , 2020b; Angulo et al. , 2021a; doi:  https://doi. org/10. 6084/m9. figshare. 12668570). All costs were adjusted for inflation through the Consumer Price Index (https://data. worldbank. org/indicator/FP. CPI. TOTL?end=2017&start=1960) and are reported in 2017-value United States (US) dollars (hereafter, $;  Diagne et al. , 2020b)  in order to be comparable across space and over time. As cost estimates in InvaCost are made under different temporal scales, we annualised the data based on the difference between the "Probable_starting_year_adjusted" and "Probable_starting_year_adjusted" (i.
e. the approximate starting and ending years of the cost; see doi: https://doi. org/10. 6084/m9. figshare. 12668570 for description) columns using the expandYearlyCosts function of the 'invacost' package in R (v4. 0. 2) (R Core Team, 2020;  Leroy et al. , 2021). Each expanded entry thus corresponded to a single year for which costs were available following this expansion process, i. e. costs spanning multiple years were divided among those same years (e. g. a single cost entry of $20 million between 1991 and 2000 in the original database was transformed to ten cost entries of $2 million annually across those ten years). Following expansion, we considered only costs that impacted years between 1960 and 2020, respectively reflecting the first year appropriate exchange rates could be determined and the last year costs were searched for.
We then performed several filtering steps to this dataset so that we obtained the most robust subset (hereafter called "filtered dataset"), considering the nature of the cost and quality of the source material. First, we filtered the database to include "observed" costs only (thus excluding "potential" costs), since we were interested in definitively realised costs over time rather than those that had been extrapolated or predicted to potentially occur either in space or time. Second, we retained only costs classified as "highly reliable" (thus excluding "low reliability" costs), meaning that we used exclusively those that were either published in peer-reviewed journals and official reports or, if they were found in grey literature, that had adequately supported, replicable analyses and justified assumptions.
Third, we filtered out "mixed" cost types (n = 560 unexpanded entries) to keep only costs that were clearly determined to be either "management" or "damage" costs (in order to place management costs into a wider context as a proportion of IAS damages). Last, we removed entries for which the management type was unspecified (for management costs only; n = 124 unexpanded entries) or where the temporal duration of the cost was unclear (i. e. missing "Probable_starting_year_adjusted" and/or "Probable_ending_year_adjusted", because the cost could not be expanded over time in those cases). Unless specified, all results are provided for the filtered dataset, but results using unfiltered data (i. e. including "low reliability" and "potential" costs) are presented initially. Our final, filtered datasets are provided as Supplementary Material 1detailed information on all descriptive variables can be found in an on-line repository (doi: https://doi.
Categorisation of management expenditures We followed the classification provided in the InvaCost database regarding management expenditures. Each cost entry was therefore classified under one of the following four categories that broadly represent different types of management actions (full list in Supplementary Material 2): 1. Pre-invasion management: proactive monetary investments for preventing alien species invasions to or within an area. Includes, among others: quarantine or border inspection and risk analyses (e. g. assessments of the risk of invasion, potential impact of invasion, etc. ); 2. Post-invasion management: reactive expenditure for managing already introduced or established populations of IAS (including eradication, containment, control, etc. ); 3. Knowledge funding: money allocated to all actions and operations that could be of interest across all steps of management at pre-and postinvasion stages (including administration, communication, education, research, etc.
Distribution and analyses of management expenditures We used a range of descriptors (see  Diagne et al. , 2020b  and doi:https:// doi. org/10. 6084/m9. figshare. 12668570 for details on these descriptors) from our filtered dataset, described earlier, to examine the monetary investments in management actions in relation to: (1) the geographic regions [Africa, Antarctic-Subantarctic, Asia, Europe, North America (including Central America), Oceania (including Pacific Islands) or South America], official countries and spatial scales (global, intercontinental, continental, regional, country, site or unit); (2) broad taxonomic groupings  [vertebrates, invertebrates, plants, others (i. e. fungi, chromists or pathogens), or diverse/unspecified] and individual species (i. e. excluding multiple or unspecified species); and.