Biodiversity indicators miss local and short-term change: A blank space waiting to be filled
Bibliographic record
Abstract
The year 2030 is rapidly approaching. Building, monitoring, and reporting indicators to evaluate the 2030 targets in the Kunming-Montreal Global Biodiversity Framework (GBF) is a major challenge that requires, at minimum, nations to assess their progress at least once within the next five years. To effectively capture this progress, we need indicators that capture fast-paced, on-the-ground biodiversity change, alongside slower, more diffuse biodiversity trends at national scales. We gathered a group of biodiversity scientists and practitioners to evaluate how well common types of indicators cover the space-time continuum of biodiversity changes. We highlight a striking, nearly unanimously agreed upon, gap in the available indicator toolbox in our ability to capture on-the-ground biodiversity changes. To fill this blank space, we call for investment in local-scale and short-term monitoring, research on how to optimize this monitoring for rapid detection, and urgent development of indicators at these more actionable scales.
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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 | 0.060 | 0.137 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.016 | 0.053 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.006 | 0.018 |
| Insufficient payload (model declined to judge) | 0.014 | 0.007 |
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".