Accelerating the research on Biodiversity and Ecosystems: Best Practice on Climate Change vs Non-Indigenous Invasive Species (NIS)
Bibliographic record
Abstract
The current knowledge on the risk of climate change for biodiversity and ecosystems needs to be improved by seeking evidence from cross-domain analyses. As a demonstration case, this study analyses how to investigate and monitor the rapid increase of Non-Indigenous Invasive Species (NIS) in European ecosystems. These species may not only replace indigenous ones but also alter habitats, interacting with the changing environment and eventually severely influence established socio-economic regimes. The challenge is to adopt a comprehensive approach by considering the bulk of the biotic and abiotic variables and their interactions, which may be even more important for the distribution of the NIS than the occurrence of the NIS. Such approaches require access to big datasets (from genomics to in situ and satellite borne environmental data) and high computational power, especially for those models with iterative algorithms. This study aims to: integrate data from different scientific disciplines in the marine subdomain (e.g. chemistry, physics, biodiversity, ecosystems, genomics, socio-economics) into an analytical framework in order to advance our knowledge on the impact of NIS on European marine biodiversity and ecosystems; to connect the analytical framework and federate access to relevant data infrastructures at the EOSC portal in order to mobilise and empower a larger community of researchers and potential data providers; and to demonstrate and promote the benefits and potential of web-based science using EOSC.A break-through Technical Composability Layer (Tesseract, which includes an additional option with Jupyter Notebook) is used in order to achieve the horizontal composability of the services. Only FAIR-compliant datasets are used in this study. However, because the nature of the project is primarily multidisciplinary and cross-domain, the only way to guarantee that the results deriving by the different disciplines/domains are comparable is to FAIR-ify the web services used, too. This way, it is ensured that both the quality and process potential in the different disciplines and domains are comparable and therefore so are their results.This paper brings together scientists making basic research on biodiversity and ecosystems, computer engineers, including software and web developers, in order to create a FAIR-compliant virtual research environment (VRE) to achieve both the scientific goals and the community engaged.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.083 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.017 | 0.026 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".