dataesr/scientific-paragraphs-categorization huggingface dataset:
For example, when several ELISA tests are available from commercial providers, each provider recommends different sets of buffers. However, as a TPS organiser is limited in terms of resources and time, some modifications (e. g. , the use of the same buffers for all of the ELISA tests) can be introduced to facilitate preliminary studies and as a final result to include more tests in the TPS and to allow more validation data to be produced and to standardise the testing conditions. The producers of commercial tests might benefit from such a study, as getting valuable information on how their tests perform under certain conditions can be of common interest; however, it is recommended that the TPS organiser communicates these adaptations to the kit providers. Communication with kit producers is important because kits are optimised with specific chemicals/ buffers, and if changes are made this might affect their performance.
Future TPS organisers should have all of these aspects in mind during the planning stage of a TPS. Additionally, information about participants equipment is required, to help the TPS organiser to plan the TPS and to interpret the results obtained. Criteria can be weighted differently to allow the selection of the participants for the defined scope c Performing all tests within one method by TPS participant will enable collection of more validation data which with the application of appropriate statistical analyses will let to the increased relevance and confidence of the TPS results.
5). Potential participants should respect the deadlines to provide the requested information. If a potential participant does not return the form with the requested data filled in by the deadline, it can be considered that they are not interested in taking part in the TPS. Responses from interested laboratories are then evaluated by the TPS organiser using the weighted criteria described above to select the best TPS participants, and their participation is confirmed by sending them an acceptance email. It is difficult when organising a TPS to estimate how many potential participants will be interested in taking part in a TPS. For the TPS organiser this information is important, because it significantly affects the TPS process, especially in terms of the preparation of the materials and to ensure sufficient participation to allow proper statistical analysis of the data.
If a participant provides results that are far from the expected results, that particular dataset may also be excluded from the analysis. However, the selected criteria and weighted values should not lead to the exclusion of too many laboratories. Indeed at least 10 datasets per test are needed to perform the statistical analysis and to provide results with high confidence (EPPO PM 7/122(1)). In addition, if the criteria used are too strict, only highly proficient laboratories will be selected and a high proportion of concordant datasets can be expected. As a consequence, the performance characteristics of the test(s) might be overestimated and might not reflect the global diagnostic community. Usually in a TPS this decision is based on the self-assessment of competence of the potential participants, which can be biased and subjective, but this is a risk that is accepted by TPS organisers.
In some cases (e. g. , when the samples are prepared as an extract of the pest), based on the previous experience with the pest and taking into account the timeframe of the TPS, when the TPS organiser considers that the stability of the samples can be affected, stability testing can be performed also during the various stages of the TPS. This practice retains as many as possible of the datasets for the final evaluation. During the TPS on detection and identification of TSWV (where the samples were prepared as extracts), stability tests were performed each week starting 7 days after the samples were dispatched to the participants. Stability testing should also be conducted after the deadline for participants to perform the analysis, to confirm that the stability of the samples has been maintained throughout the TPS.
If samples are not stable and homogeneous, they can affect the assigned values, and their impact on the evaluation of the results should be estimated. Such samples might be rejected for use in the TPS, or they can be used under specific conditions that should be clearly stated. If the participating laboratories evaluate measurements or tests on test items (e. g. , the samples) that are not considered stable any more, these datasets should be excluded from further evaluation (e. g. , if the laboratory returns the results too late). For the TPS for TSWV, stability was carefully assessed because a plant extract known to be unstable to orthotospoviruses was used. During the preliminary studies, aliquots of the last three positive dilutions of one TSWV sample were stored at À20 C and analysed with the eight tests after 2, 5 and 8 weeks of storage.
Collecting the TPS Results and Analysing the Data The TPS participants should send back the completed results forms to the organiser or complete an online results form. The TPS organiser analyses the TPS results and summarises the conclusions on the performances of the tests. During the analysis of the results, the TPS organiser might have to exclude all or some results from some TPS participants; e. g. , if there is suspicion of contamination during the processing of the samples, or if the analysis was not performed according to the protocols provided. If there were any deviations from the recommended protocols by any of the participants, this should be recorded, and their potential impact on the results should be assessed. The performance of the tests is evaluated through determination of their performance characteristics, and through comparisons of the performance characteristics of the different tests (or methods, if the same panel of samples was used).
To perform the correct statistical analysis, a TPS requires a minimum number of participating laboratories; i. e. , at least 10 valid laboratory datasets per test (EPPO PM 7/122(1);  Chabirand et al. 2017 ). However, it is recognised that this might be a constraint in plant-pest diagnostics. If the results of less than 10 laboratories were used to evaluate the performance of the tests, the report should contain a disclaimer that the results might not be reliable, and that any deviation might be the result of the processing of an insufficient number of datasets. The main performance characteristics for validation of tests include the following (EPPO PM 7/98(5) and PM 7/122(1)): • Analytical sensitivity • Analytical specificity, inclusivity • Analytical specificity, exclusivity • Diagnostic specificity • Diagnostic sensitivity • Selectivity • Repeatability • Reproducibility • Robustness • Accuracy After obtaining the data from the TPS participants, the TPS organiser needs to carry out the full evaluation.
The first step is the definition of the outlier results. The TPS organiser can decide how to define the outliers on a case-by-case basis. Frequently, controls provided to participants with the test panel are used as a quality check of the datasets, and only the datasets with concordant results for all the controls are considered as valid. Additionally, the TPS organiser can use other methods to identify outliers; e. g. , for the TPS for TSWV, the results of the healthy tomato samples were assessed, and datasets of laboratories where there were two or more (out of three) false positive results for the healthy tomato samples were also excluded. Another option to identify outliers can be to use different graphical tools, such as heat maps, which allow rapid visualisation of outlier results that are too far away from the results of the other laboratories (Fig.
3. 2). If needed, in particular cases, additional outliers can be defined, such as incomplete datasets. Incomplete datasets and datasets with incorrect results of controls can be considered as the first level of outliers to be excluded. Outliers identified only through graphical analysis should be checked carefully in terms of whether they should indeed be excluded or whether they are a part of the method robustness. The next step in the results analysis is to evaluate the performance of the individual tests. First, the performance can be described in terms of the number and proportion (%) of the results that were inconclusive, true negatives (negative agreement), false negatives (negatives deviations), true positives (positive agreements) and false positives (positives deviations). The number and proportion (%) of concordant and non-concordant results can also be calculated.
For example, in the TPS for TSWV, there were problems during the analysis of the raw data obtained from the TPS participants for the real time RT-PCR tests. It was apparent by the "raw" results received from the TPS participants that some results were not interpreted correctly (taking into account all provided Cq values), and some laboratories had clear problems with contamination. Therefore, some of the results submitted by TPS participants were corrected to retain as many datasets as possible in the analysis. This was necessary because the TPS participants used different approaches to set up Cq cut-off values: some set the Cq cut-off value at a too low level, while some did not use any Cq cut-off value even though they had some late signals in the negative controls. Cq cut-off values are equipment, material and chemistry dependent, and need to be verified in each laboratory before the tests are implemented.
However, it was not possible to check this for all of the TPS participants separately. Therefore, a fixed Cq cut-off value of 35 was introduced for all of the RT-qPCR datasets except when the Cq cut-off value applied by a TPS participant appeared to be reasonable. This and similar modifications can be carried out by the TPS organiser to respect the 'trueness' of the results, and to keep as much data as possible in the calculations of the statistical parameters, which will provide more reliable measurements of the performances of the tests. Below, an example from the data analyses of the TPS for TSWV is presented. First, each test was analysed separately (Table  3. 10), and then the tests within each method were compared (Table  3. 11). The colour coding is consistent across Tables 3. 10 and 3.
13). These data demonstrate that it is important that the TPS includes samples with non-targets that might react in tests that are not highly specific (with their evaluation not only in one laboratory). Analyses of TPS results should also include a comparison with the results of the preliminary studies. If a significant discrepancy occurs in any of the diagnostic parameters, this should be examined and explained, and the TPS organiser should clearly state whether this discrepancy affects the conclusions of the TPS, and whether the results of the TPS are valid, and to what extent. Some of the possible contributions to the differences in results might relate to different preparation protocols for the samples (e. g. , use of slightly different buffers, heat deactivation of the extract, storage of the samples, addition of some chemicals).
Reports and Dissemination Activities After the organiser of a TPS has carried out the analysis of the data from the TPS, the reports should be distributed to the participants, and results can be published in scientific articles or presented in different conferences, stakeholder meetings, etc. If the TPS included commercial kits/tests, it is recommended that the organisers of a TPS arrange multilateral and/or bilateral meetings with the producers of any commercial tests or kits included, to discuss the results obtained with them prior to finishing the TPS report. The aim of such a meeting is to communicate the results obtained to the producer (company, or in some cases scientific organisation producing the test), to discuss the performances of their tests, and to understand the reasons of unexpected results (if any).
The common template consists of a front page where the basic data on the TPS report should be provided, such as the title of the TPS, the organiser and the contact data, and the version of the TPS report. These are followed by a common outline for all TPS. This outline consists of the following main parts: Context of the application; Methodology of the evaluation; Preliminary studies for evaluation of the method performance; The test performance study; Conclusions; and References. An example with this outline is given in Appendix 8. TPS organisers can adapt the outline based on their needs. For all TPS organisers, it is mandatory to have a disclaimer at the beginning of the TPS report, which states that the results presented in the TPS report only reflect the specific case study and the associated performance results of the commercial reagents at the time when they were included in the study.
In this section we present the steps taken in the VALITEST Project to maximise the impact of the work carried out in the framework of the TPS, and to stimulate, optimise and strengthen the interactions between the stakeholders in Plant Health for better diagnostics. During the Project, the partners established better contacts with the scientific community, stakeholders and policy makers to ensure better dissemination of the TPS results. Furthermore, the links that they established with regional and international standardisation bodies enabled widespread dissemination of the validation data obtained by the Project partners, in particular via the freely available and accessible EPPO database on diagnostic expertise (https://dc. eppo. int/). The EPPO database on diagnostic expertise is a large database that contains the inventory of the diagnostic expertise available in the EPPO region and provides access to the validation data for diagnostic tests for regulated pests.
Also, the TPS organiser should comment on the performances of certain tests, in terms of which uses and conditions they are applicable to, and which they are not. As mentioned several times, open communication between the TPS organiser and the TPS participants is very important. After the completion of the entire TPS, organisers can circulate a satisfaction survey to solicit feedback from the participants. The participants can be asked to rate the experience of their participation, their satisfaction with the organising process, and the possible areas for improvement. Certainly, such surveys should be confidential, and in line with the General Data Protection Regulations (GDPR for the EU region). Even though a TPS is not designed as an interlaboratory comparison where the proficiency of the laboratories is evaluated, after each laboratory receives the TPS report, they can analyse their own performances.
However, TPS studies represent the best way to obtain the most reliable validation data for a test of interest, and to study the performance of the test in different laboratories under different conditions. This can help to minimise the uncertainties in the performance characteristics of a test. In addition, TPS are also the most transparent approach to discuss tests and testing strategy with different stakeholders. It provides objective evidence for each test evaluated, despite it being carried out under specific conditions. By organising 12 TPS on 11 prioritised pests, we aimed to reach the widest possible audience of interested stakeholders in the field of plant pest diagnostics. In all, 640 diagnostic laboratories were invited to participate in these 12 TPS, and 242 of these laboratories confirmed their participation.
italics text, need to be determined by the TPS organiser validation data and other data about tests (e. g. , matrices tested, processing of samples) can be collected through literature searches, internet searches, database searches, experience of the TPS organiser, EUPHRESCO final reports (https://www. euphresco. net/projects/portfolio), dedicated questionnaires or surveys on diagnostic tests used in different laboratories, and validation data from discussions with commercial kit providers. The authors of tests from scientific publications can also be contacted to obtain additional validation data on published tests. Sometimes, validation data for a test will exist, but might not be publicly available, or only be partially available. This communication is time consuming, but it enables a TPS organiser to make the best decisions in the test selection process.
Acknowledgements The authors would like to thank  Jenny Tomlinson  and  Lynn Laurenson  from  Fera Science Ltd. (FERA)  for their help during the preparation of this book. We are thankful to all of the colleagues who participated in the  VALITEST  Project, to all of our colleagues who provided their isolates for the TPS organisers, and to all of the laboratories that took part in the 12 TPS organised in the framework of the  VALITEST  Project. We would like to specifically thank  Sébastien Massart  and  Bénédicte Lebas  ( University of Liège, ULG ) for useful discussions on the composition of the sample panel with regard to statistical analysis of the data, and  René van der Vlugt  ( Wageningen University & Research ) for preparation of the guidelines on the reference materials. We also thank our colleague  Dr Maja Zagorščak  from  NIB  for her help with R codes for the Heatmap.
g. , ready to use reagents) Availability and relevance of Yes/no Medium High controls (in the case of kits) Protocols Available detailed protocols Yes/no Yes High High Simple test procedure Yes/no Yes Low High Simplicity of data analysis Yes/no Yes Low High User friendly test Yes/no Yes Low High Time needed to complete Duration in time unit Fastest Low High analysis (<1 h/ 1 day/ several days) Easy to multiplex Yes/no NA Low Low Database/library dependent NA NA NA NA (yes/no) (e. g. , fatty acids profiling, sequencing) (continued).
g. , rou- dation data submitted to if validation data (validation data: tine analyses, method EPPO database, or other submitted/ An advantage) developments, publica- publications published tions, participation in congresses) Previous participation in Yes/no Yes Medium proficiency tests or TPS Constraints for delivery Yes/no (if yes, explain) No Medium Any problems or limita- Yes/no (if yes, explain) Preferably no Medium tions with delivery on dry ice Yes/no Yes Medium (continued).
3 and NorESM1-M) and CMIP6 (CNRM-CM6-1, CESM2) database using the highest-greenhouse-gas-concentration pathways (respectively RCP8. 5 and SSP5-8. 5). A description of MAR and the experiments is given in Sect. 2. Section 3 details the regional evolution of the SEB and its different components over the AIS and provides analysis of the physical drivers behind differences between projections. Finally, our results are discussed and summarised in Sect. 4.
Forcing datasets and experiments Large-scale conditions are prescribed every 6 h at the MAR boundaries. The forcing fields include information about air temperature, specific humidity, zonal and meridional wind speed components, and at the surface they include information about pressure, sea temperature, and sea ice concentration. MAR is also nudged in the upper atmosphere by largescale temperature and wind components to constrain its atmospheric circulation  (Agosta et al. , 2019). Most of the projections of the Antarctic surface melt have been based on direct outputs of ESMs (e. g. Seroussi et al. , 2020)  from CMIP5 or derived from them using statistical regressions (e. g. Trusel et al. , 2015) , while more recent climate models from CMIP6 now project stronger warmings at both regional (Antarctic) and global scales.
Recent studies with MAR  (Le Toumelin et al. , 2021; Hofer et al. , 2021)  have revealed significant changes in LWD due to drifting snow, a process not modelled in our study, suggesting that drifting snow could further contribute to the spread in melt projections. Furthermore, MAR seems to underestimate the present sum- mer LWP compared with CloudSat-CALIPSO estimates. Our study highlights the sensitivity of the future surface melt to liquid-containing clouds, whose representation is considered a challenge for climate models in Antarctica (Listowski and Lachlan-Cope, 2017;  Vignon et al. , 2021). Future work should improve the cloud representation (including in MAR), potentially leading to revised melt projections over the Antarctic ice shelves. Code and data availability. The MAR code used in this study is tagged as v3.
11. 1 on https://gitlab. com/Mar-Group/MARv3. 7 (last access: 28 May 2022)  (MARTeam, 2021). Instructions to download the MAR code are provided on https://www. mar. cnrs. fr (last access: 28 May 2022)  (MAR Model, 2021). The MAR version used for the present work is tagged as v3. 11. 1. The MAR outputs used in this study are available on Zenodo (https://doi. org/10. 5281/zenodo. 6406158;  Kittel, 2022). Other higher-frequency MAR results and Python scripts are also available upon request by email (ckittel@uliege. be). Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/tc-16-2655-2022-supplement. Author contributions. CK designed the study, ran the simulations, made the plots, performed the analysis, and wrote the manuscript. ChA and XF provided important guidance, while all the authors (CK, ChA, SH, CéA, NCJ, EG, LLT, ÉV, HG, and XF) discussed and revised the manuscript.
Acknowledgements. We acknowledge the  World Climate Research Programme 's  Working Group on Coupled Modelling , which is responsible for CMIP, and we thank the climate modelling groups for producing their model output and making it available. Finally, we would like to thank  Rajashree Datta  and the other anonymous reviewers for their constructive remarks that helped to improve the paper. Financial support. This research has been supported by the  Fonds De La Recherche Scientifique (FNRS)  and the  Fonds Wetenschappelijk Onderzoek-Vlaanderen (FWO)  (under the  EOS  project no. O0100718F  and grant no. T. 0002. 16 ). Computational resources have been provided by the  Consortium des Équipements de  Calhttps://doi. org/10. 5194/tc -16-2655-2022 The  Cryosphere , 16, 2655-2669, 2022.
C. Kittel et al. : Physical drivers behind future ice-shelf melt cul Intensif (CÉCI), funded by the Fonds de la Recherche Scientifique de Belgique (F. R. S. -FNRS) under grant no. 2. 5020. 11, and the Tier-1 supercomputer (Zenobe) of the Fédération Wallonie Bruxelles infrastructure, funded by the Walloon Region under grant agreement no. 1117545. Christoph Kittel and Nicolas C. Jourdain have received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement no. 101003826 via the CRiceS (Climate Relevant interactions and feedbacks: the key role of sea ice and Snow in the polar and global climate system) project.
Grazing incidence Small angle scattering (GISAXS) measurement. GISAXS was performed on the Xuess instrument equipped with an Excillum MetalJet liquid gallium X-ray source. Alignment was performed on silicon substrates via three iterative height (z) and rocking curve (Ω) scans, with the final grazing incidence angle set to Ω = 0. 2º. Scattering patterns were recorded on a vertically-offset Pilatus 1M detector with a sample to detector distance of either 332 or 553 mm, calibrated using a silver behenate standard to achieve a qrange of 0. 045 -2. 0 Å -1. Two-dimensional images were recorded with exposure time of 600 s. The images were masked to remove the sample horizon, detector module gaps and beamstop and radially integrated from the apparent beam centre. Data correction and reduction was performed using the GIXSGUI MATLAB toolbox  42.