CCGenetics/guidelines-genetic-diversity-indicators: Guideline materials and documentation for the Genetic Diversity Indicators of the monitoring framework for the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
In December 2022, 196 Parties to the CBD adopted the Global Biodiversity Framework (GBF) and with it the Ne 500 and Populations Maintained indicators as headline and complementary indicators, respectively, in the accompanying global monitoring framework (Annex 1 of CBD/COP/DEC/15/5). The following guideline materials are intended to assist nations in quantifying genetic indicator values at every stage of the process: from species selection to data compilation to indicator calculation. The materials are based on the co-creation experience of the first pilot multinational assessment of the genetic diversity indicators, and will be regularly kept updated through a versioning system as more teams share their experience. The first release was submitted to Biodiversity Informatics as "Training module" and presented at the 16 April 2024 CBD Webinar on the guidance from the AHTEG on indicatros for the GBF.
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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 | 0.011 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.098 |
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".