Test d’applicabilité des standards d’échange Darwin Core et SOT V3.0 aux données de surveillance de la biodiversité
Bibliographic record
Abstract
Biodiversity monitoring is essential to track its evolution and take action to halt its decline. This requires the implementation of monitoring protocols at all levels of biodiversity and territory, promoting the harmonisation of data collection for use in decision-making by politicians, associations and researchers. However, this diversity of stakeholders and their needs can be scientifically incompatible - leading to difficulties in exchanging data.Standardisation is a process of technical harmonisation of biodiversity data in the form of a data exchange standard. It is a tool used by French and international information systems as a structural guide for associated biodiversity databases. Its aim is to improve the consistency and quality of data with a view to disseminating and exploiting it. In this report we test the applicability of two data exchange standards - the international Darwin Core standard and the French standard Observations et suivis de taxons V3.0 - to five datasets from biodiversity monitoring systems. The strengths and weaknesses of the standards for each specific standardisation are reported, suggestions for development are made, and their coexistence is discussed.
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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 | 0.153 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".