Selecting indicators to track progress towards the Global Biodiversity Framework: a case study of Quebec's 2030 Nature Plan
Bibliographic record
Abstract
Selecting biodiversity indicators to report national and subnational progress towards the Kunming-Montreal Global Biodiversity Framework (GBF) is a major challenge, one made even more urgent by the fast-approaching 2030 targets. To identify appropriate indicators, the selection process must be streamlined, while remaining transparent, effective, and with the active engagement of stakeholders from the academic, public, and private sectors. We present guidelines for the selection of biodiversity indicators to track progress towards 2030 targets in the context of the GBF, with a case study of the province-level indicator recommendation process for Quebec's 2030 Nature Plan. We outline six steps to develop a shortlist of indicators that are relevant to targets, fulfill minimum criteria of scientific quality given available biodiversity data, and practical to inform decisions and on-the-ground conservation actions. We present the rationale and outcomes of this selection process, culminating in 15 biodiversity indicators that we recommended for Quebec's 2030 Nature Plan. Going forward, we recommend continuing to build trust across sectors, developing communication guidelines to standardise indicator reporting, and testing indicator performance at national and subnational scales. Overall, this case study demonstrates that with active engagement and cooperation, we can rapidly rise to the challenge of identifying the indicators we need to track biodiversity change.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".