dataesr/scientific-paragraphs-categorization huggingface dataset:
Pseudo-3D Extension and Robustness to Changes of Acquisition Parameters We conducted another experiment with different acquisition parameters: incident beam 80keV, distance sample-detector 270mm, distance source-detector 92m, pixel spacing 1. 22µm, angular span 360°over 3500 projections, detector size 1424 × 1024 pixels. The sample is larger than the detector. To accommodate for this, the centre of rotation of the sample is off compared to the central column of the detector. To avoid sample truncation in the projection domain for the registration optimisation, it is necessary to reformat the projections using redundant information so that they span over 180°, and the centre of rotation and the central column of the detector becomes aligned. Figure  9  shows that the new dataset is corrupted by similar streak and phase contrast artefacts.
Quantitative Evaluation of the Objective Functions Figure  10  presents the distributions of ZNCC between the experimental CT slice and the final simulated CT slice for the 25 runs of each objective function using boxplots. Our assumption is that the objective function providing the highest correlation and smallest spread is the best method, i. e. RMSE on Sino with normalisation. We computed the -values between the distribution of ZNCC for the  25    with normalisation as our baseline as it is significantly better than all the other objective functions. However, there are a few outliers that need to be investigated. We can identify these outliers using a parallel coordinate plot (see Figure  11 ), a technique popularised by Alfred Inselberg  [16]. Such plots enable us to visualise and analyse high-dimensional datasets.
Phase contrast is by far the longest to tune, by a large factor. Table  4  also shows that the desktop system performs faster than a GPU node on the supercomputer. Using the supercomputer, it took 1 hour and 47 minutes on average per registration for RMSE on Sino with normalisation. The desktop computer was 1. 40× faster (it only took 1 hour and 17 minutes on average). The same pattern is observed for ZNCC on , the desktop computer was 1. 16× faster (34 minutes vs 29 minutes); for DSSIM on  with normalisation, the desktop computer was 1. 41× faster (1 hour and 39 minutes vs 1 hour and 10 minutes). The reasons for this difference are hard to pin point as the systems (both hardware and software) are different. NVIDIA Tesla V100 on SCW are ten months older than the NVIDIA GeForce RTX 2080 Ti on the desktop computer.
To further improve this performance, it is possible to use a more recent GPU on a desktop computer, or use several GPU nodes at the same time. In this case every GPU node would register a different subset of the whole dataset. Streaks and phase contrast artefacts in the simulated slices are, again, consistent with the experimental data (see Figure  22 ). Figure  23  shows a blend comparison between a region of interest (ROI) of the first and last slices of the real experiments, and of the simulated slices after registration. It shows that the tilt of the fibres is also recovered. Once the geometrical properties extracted, it is possible to generate a perfect 3D CT scan without artefacts using Equation 3. Figure  24  shows the first slice with and without artefacts. Table  9  provides a comparison of the average ZNCCs for images simulated i) without artefact (i.
he paper ccepted, lisher l embed video A difficulty with this new experimental dataset was that the sample was larger than the detector. Consequently, the centre of rotation was off by a large margin and an angular span of 360°was used. When reformatting the projections over 180°to avoid sample truncation in the projection domain, misalignment of the centre of rotation with the central column of the projections may occur. The consequence of this misalignment was that the phase contrast artefact around a fibre would not be rotationally symmetrical, which affected the optimisation of the phase contrast parameters.
Conclusion Our aim was to show that a realistic projection model is required to automatically produce an accurate CAD model of a material from X-ray tomography when CT data is so corrupted by artefacts that geometric analysis is challenging. Our model takes into account geometrical properties as well as beam hardening, impulse response of the detector, phase contrast, and photon noise. Every unknown of our model was automatically tuned using a stochastic optimisation algorithm: CMA-ES. It minimises the discrepancies between the projection data acquired at ESRF and the projections simulated using our model. The code was implemented on GPU using gVirtualXRay, an open source tool for simulating X-ray images. 14 different objectives functions were evaluated on a supercomputer. Our results showed that the CT image reconstructed from the simulated data is 93.
Acknowledgements We thank  Jean-Yves Buffière  and  Ce Xiao of MATEIS laboratory (Lyon) , and  Wolfgang Ludwig of ESRF (Grenoble)  for the new projection and CT data. We also thank  NVIDIA Corporation  for the donation of the NVIDIA TITAN Xp GPU used in the development and validation of gVirtualXRay, and  Supercomputing Wales  for the use of its supercomputer.
Subtracting the climate signal helps to reveal the inhomogeneities in the test series. One limitation arises, however, when GNSS data are processed in difference mode, whereby the errors of nearby stations might be highly correlated. Another approach also widely used is to include a parametric representation of the climate signal in the segmentation model for the test series, e. g. , a periodic variation in the mean accounting for seasonal variations in monthly data  [20]. A number of methods, including tests and optimal and sub-optimal optimization methods, have been assessed for homogenizing GNSS IWV series in a benchmark exercise conducted in the framework of COST Action GNSS for severe weather and climate change (GNSS4SWEC)  [21]. The results were published by  [9] , who concluded on the superior performance of the maximum likelihood methods, including the segmentation method that we propose in this paper.
Our method actually yielded the best change point detection performance for different sets of synthetic data (e. g. , the most complex synthetic data included IID Gaussian noise, autocorrelation, and linear trends). The first specific characteristic of our method, hereafter called segfunc, is heteroscedasticity of the noise component. It is well known in the segmentation framework that heteroscedasticity is a severe limitation in existing procedures that do not account for variations in the noise variance  [22]. In a previous work, we proposed a preliminary version of this segmentation method, hereafter called segonly, designed to detect abrupt changes in the mean in the presence of heterogeneous variance that is assumed to vary on a monthly basis  [8]. The second feature introduced in segfunc is a functional part that is required to model the presence of a periodic bias in the IWV difference data.
This bias originates from the fact that the reference series used to subtract the climate signal does not perfectly represent the seasonal signal contained in the observed GNSS IWV data. In this work, as well as in the benchmark study of  [9] , the reference IWV data are taken from the European Center for Medium Range Forecasts (ECMWF) ERA-Interim reanalysis  [23]. Using a reanalysis is a convenient solution when the station network is too sparse to form differences between stations. A drawback is that the GNSS point observations and the reanalysis grid cells show representativeness differences, mainly in coastal and mountainous regions, which often have a significant seasonal periodic component  [24]. Figure  1  shows an example of a daily IWV difference series and the segmentation results with the segonly method of Bock et al.
R Packages The method was initially implemented in an R package named GNSSseg, which is available on the CRAN. A more recent and faster version named GNSSfast is now also available from the Git repository https://github. com/arq16/GNSSfast. git (accessed on 12 June 2022). GNSSfast integrates a faster version of the DP algorithm, based on R package gfpop, proposed by Hocking et al. [43] , with an algorithmic complexity in O(Kn log n) for the segmentation step. We evaluated empirically the time improvement of GNSSfast on an excerpt of ten series from the application data set used in Section 3 with length varying between 5000 and 6000 points. The mean time over the ten series of the procedure is 41 min (2463 s) with GNSSseg against 1. 32 min (79 s) with GNSSfast on a standard PC workstation with a Ubuntu 18.
Data, Metadata, Outlier Detection, and Validation Procedures The data set consists of daily IWV differences (GNSS minus ERA-Interim reanalysis), for 120 global GNSS stations, for the period from 1 January 1995 to 31 December 2010  [24, 44]. A map of the station network can be found in  [24]. The metadata include equipment changes and processing changes (the latter are specific to the particular reprocessed data set used in this study  [45] , but they concern only a few stations in 2008 and 2009; this issue is further discussed in Parracho et al. [6] ). The equipment changes were extracted from the IGS site-logs (https://files. igs. org/pub/station/log/, accessed on 12 June 2022). They consist of the dates of receiver (R), antenna (A), and radome (D) changes. Experience showed that not all equipment changes produce a break in the GNSS IWV time series.
The most important ones are antenna and radome changes  [7]. However, there is some evidence that changes in the receiver settings can also produce inhomogeneities  [5] , especially changes in the elevation cutoff angle setting. Unfortunately, the elevation cutoff angle settings have not been reported in the IGS site-logs in the early periods (mainly before 2000). The GNSS IWV estimates are also impacted by environmental changes that are not reported in the IGS log-files, such as changes in the electromagnetic reflections and scattering properties of surfaces around the receiving antenna and changes in the satellite visibility (e. g. , due to growing vegetation or urbanization). As a consequence, although the metadata extracted from the IGS site-logs represent a valuable source of validation, they may be incomplete and a perfect matching between our detected change points and the IGS metadata is thus not to be expected.
These dates are then compared to the results from the three criteria and the closest solution is accepted partly or totally. In this selection, priority is given to the change points close to known changes composed of the GNSS metadata extracted from the IGS site-log files, as well as daily quality check information produced with TEQC software (a software program widely used for the translating, editing, and quality checking of raw GNSS measurements  [49] ). When the choice is difficult, higher priority is also given to the solution with the smallest number of change points. For each station, the change points selected by each of the three criteria are either rejected or accepted and a corresponding flag is set. In case a change point is accepted, its date can be either set to the date of the nearest metadata, or to a better one than the nearest one (e.
Funding: This work was developed in the framework of the  VEGA  project and supported by the  CNRS program LEFE/INSU. The contribution of the third author has been conducted as part of the project  Labex MME-DII  ( ANR11-LBX-0023-01 ) and within the  FP2M federation  ( CNRS FR 2036 ). Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable.
Data Availability Statement: The GNSS IWV data are available from https://doi. org/10. 14768/063 37394-73a9-407c-9997-0e380dac5591 (accessed on 12 June 2022;  [44] ). ERA-Interim data are avaialable from https://www. ecmwf. int/en/forecasts/datasets/archive-datasets/reanalysis-datasets/erainterim (accessed on 12 June 2022; [23]).
, 2010)  and the eradication of non-native plants  (Hulme, 2020). One particular feature of New Zealand-the only remote archipelago that is a developed nation independent of any continental authority-positions this country well to be a world leader in IAS management  (Simberloff, 2019). However, despite its successes and place at the forefront on the global stage, the country still suffers from invasion debts (i. e. , the time-delay before an already introduced species becomes invasive;  Sheppard, Burns & Stanley, 2016; Brandt et al. , 2021)  and incursions of new known IAS that pose substantial ongoing challenges to management. The first global compilation of reported invasion costs ('InvaCost') has recently been published  (Diagne et al. , 2020; Diagne et al. , 2021a) , providing the most comprehensive, synthesised database of IAS economic impacts to date.
It also provides a standardised living platform for the reporting, comparison and synthesis of invasion costs in the future, enabling continuous updating by scientists, managers and stakeholders  (Diagne et al. , 2020; Diagne et al. , 2021a). While limitations remain, as awareness and uptake of this platform increases, it will be invaluable to IAS research. For example, a lack of easily accessible damage costs can impede efficient allocation of targeted management across species or habitats  (Ahmed et al. , 2022). Using data available from the InvaCost database, we summarised and described reported economic costs of invasions in New Zealand across approximately the last fifty years. Assessments of IAS in New Zealand already exist, but are typically small-scale or limited to specific economic sectors or taxonomic groups  (Hackwell & Bertram, 1999; Barlow & Goldson, 2011; Allen & Lee, 2006; Nimmo-Bell, 2009; Clout, 2011; Saunders et al.
Data collection To describe the cost of invasions in New Zealand, we used cost data collected in the latest available version of the InvaCost database (v4. 0), as of June 2021  (Diagne et al. , 2020; Angulo et al. , 2021) ; data openly available at https://doi. org/10. 6084/m9. figshare. 12668570). Full details on the process of the literature search are provided elsewhere  (Diagne et al. , 2020). Briefly, three online bibliographic sources (Web of Science, Google Scholar, and the Google search engine) were examined using a series of carefully composed search strings, creating standardised searches within the peer-reviewed and grey literature for the economic costs of IAS. Such costs could be presented at any taxonomic level, spatial scale or time period and to any economic sector. All cost entries were standardised to a common and up-to-date currency (2017 US$), although here we also report them as 2017 NZ$ (US$ ×1.
4 as of 1 Jan 2017) for national context. The official market exchange rates were obtained from the World Bank Open Data and adjusted using an inflation factor that accounted for the changes in US$ since the cost estimation year using consumer price indices  (Diagne et al. , 2020). Using the ''Official_country'' column within the database, we filtered entries for New Zealand, including all reported costs from 1960-2020 to ensure accurate cost standardisation and the latest year from which costs have been collected respectively (n = 2 pre-date this point  [1883 and 1945] ). We report results as both cost values and number of reports-the latter reflecting a standardised measure (i. e. , costs per year) of cost reporting effort for a given variable. We note that the searches made to compile the database may under-represent groups such as microbes (e.
Estimating total costs Deriving the cumulative cost of invasions over time requires consideration of the duration of each cost occurrence. We calculated this cost duration as the number of years between the database columns ''Probable_starting_year_adjusted'' and ''Probable_ending_year_adjusted''. In a few instances, costs were omitted (n = 12) because they had unclear durations because either the start, end or both years were not specified in the original source. We opted to remove these entries to avoid biases when assessing temporal dynamics of costs (i. e. , with costs specified over too few or many years). To calculate the total cumulative cost, we first standardised all the cost entries on an annual basis for their defined period of occurrence. Hence, for example, a single cost entry recorded as occurring over a six-year period was transformed to six cost entries, with the total cost divided to get an annual cost that was repeated for each of the six entries.
Our dataset therefore initially consisted of 810 annualised cost entries from 124 unique references. Invasion costs were then considered and estimated according to six descriptive columns present in the database (see  Diagne et al. , 2020  and https://doi. org/10. 6084/m9. figshare. 12668570 for complete details on these descriptors): (i) ''Method_reliability'': a conservative but objective evaluation of the traceability of the cost estimation (''High'' vs ''Low''). This is based on the type of publication and method of estimation i. e. , peer-reviewed or other official documents from the grey literature are likely validated prior to publication so are classified as ''High'' reliability. Other materials were only classified as ''High'' if the original sources, assumptions, and methods were accessible and fully described.
(vi) ''Species'': the taxonomic nomenclature of the species causing the cost. We focussed the majority of our analyses on a conservative approach to the reported costs, retaining only those which were graded as both observed (i. e. , had actually occurred) and having 'high' reliability (i. e. , a level of peer review or method reproducibility), hereafter called 'robust' costs. To analyse the economic costs of IAS over time, we used the summarizeCosts function in the R package ''invacost''  (Leroy et al. , 2022). With this function, we calculated (i) the average annual costs over the entire 1960-2020 period (in 2017 US$), and (ii) the total costs per decade. This temporal analysis was performed separately for management and damage costs. The InvaCost database includes data covering a range of spatial scales from site-specific to national level.
, 2020). Finally, we used natural log-log linear regressions to examine the relationship between Gross Domestic Product (GDP) and total (a) pre-invasion management (b) post-invasion management and (c) damage costs for New Zealand, as well as for all other countries represented in the InvaCost database (using the same filtering system to include only highly reliable, observed costs between 1960 and 2020 with specified temporal durations in each case). Accordingly, three linear models were fit with each of these log-transformed cost types as a response variable, and with log-transformed GDP as an explanatory variable per country. From these we extracted the ratio of [observed over expected] expenditures within a country for pre-invasion and post-invasion management, i. e. , the residuals of the GDP/expenditure correlation.
RESULTS From the InvaCost database we obtained 124 unique cost records encompassing a minimum of 52 species (a number of entries did not define specific species in their assessments, see Table  S1  for a full list) that corresponded to 810 annualised cost entries between 1968-2020, summing to a total reported value over this period of US$69. 09 billion (NZ$96. 73 billion). Over half of the cost entries (n = 453) were directly observed from actual costs, with the remainder being predicted, potential costs. Of the directly observed cost entries, over four fifths (n = 368) were classed as highly reliable, with a total figure for these highly reliable, observed costs of US$8. 83 billion (NZ$12. 36 billion, Fig. 1 ). The following analyses focus only on these highly reliable, observed costs, hereafter called 'robust' costs.
While reported post-invasion management expenditure overwhelmingly predominated over pre-invasion expenditure, costs associated with the latter-e. g. , in terms of biosecurity staff salaries, facility costs, research grant breakdowns etc. -are rarely within the public domain (for example as a result of privacy legislation) and thus are unlikely to be captured within this database. This makes a completely accurate comparison between proactive biosecurity management (i. e. , that undertaken at early invasion stages) and post-invasion control/eradication challenging due to data deficiencies. However, New Zealand now invests a relatively high amount in IAS prevention activities such as pathway management border controls, and is a world leader in pre-border pest risk management (Brenton-Rule,  Frankel & Lester, 2016; Hulme, 2020).
, 2021). Although the overall management spend in New Zealand does not appear sufficient to maintain pace with reported damages, the specific focus of investment into protecting taonga (i. e. , 'treasure'-culturally significant species that shape Mātauranga Māori (Māori knowledge));  (Collier-Robinson et al. , 2019) , often extremely successfully  (Allen & Lee, 2006; Bellingham et al. , 2010; Towns, West & Broome, 2013) , with their less readily monetised benefits may also explain some of this difference. This approach may even increase in the coming decades with the Predator Free 2050 program (Department of  Conservation, 2020) , although this also has clear long-term objectives to reduce both management and damage related expenditure. Indeed, when disentangling pre-invasion from post-invasion management in New Zealand, proportional spending on early-stage management was considerably higher than in many other countries within the InvaCost database when considering their economic output.
This stresses the relative scale of funding allocation to early-stage investments in New Zealand, and highlights the potential for further gains. In addition to differential reporting of ecological costs, there are other costs of biological invasions missing from the database for a range of reasons. There are undoubtedly true gaps in reported damage costs, and consequent lack of, or limited, investment in management. These include introduced game animals e. g. , chamois (Rupicapra rupicapra) and common pheasant (Phasisanus colchicus) and freshwater fish such as trout (Salmo trutta), which engender conflict through opposing values on whether they are pests (thereby incurring a damage cost) or resources (thereby not requiring control and, indeed, providing economic benefits)  (Russell, 2014).
The information contained in the unique source of cost data analysed here provides a basis for helping to guide and encourage open reporting practice to facilitate research, management, and active steps to address the socio-economic impacts of IAS in New Zealand and beyond. We again stress that knowledge gaps and inaccessibility of potentially incurred costs make the costs reported here substantial underestimates. Nonetheless, as a centralised platform for standardised cost reporting, we encourage researchers and organisations to submit their data to the InvaCost database to enhance the comprehensiveness of future cost appraisals as biological invasions, their impacts and mitigation measures ensue. This will ensure a more complete understanding of economic costs and benefits under different strategies, a critical requirement for successful prevention and management of biological invasions.
While "articles" is used for Wikipedia entries, "papers" is used to denote academic studies referenced on Wikipedia articles. Simultaneously, we also searched the EuroPMC database, using COVID-19, using SARS-CoV2, SARS-nCoV19 as keywords to detect scientific studies published about this topic. Thus, 30,000 peerreviewed papers, reviews, and preprint studies were retrieved. This set was compared to the DOI citations extracted from the entirety of the English Wikipedia dump of May 2020 (∼860,000 DOIs) using mwcite. Thus, Wikipedia articles containing at least one DOI citation were identified -either from the EuroPMC search or through the specified Wikipedia project. The resulting "COVID-19 corpus" comprised a total of 231 Wikipedia articles -all related to COVID-19 and based on at least one academic source.