A climate-biodiversity funnel that accelerates action towards global goals
Bibliographic record
Abstract
Joint action on climate and biodiversity is urgently needed to meet the goals of the Paris Agreement (PA) and Kunming-Montreal Global Biodiversity Framework (KM-GBF). Here, we analyse interlinkages between targets in these two landmark international agreements. We find recognition of climate-biodiversity interactions in the agreement texts (three KM-GBF Targets and four PA Articles), demonstrating scope for formally integrated action. Quantitative analysis indicates that climate-biodiversity interactions generate a ‘funnel’ (bounded by 0.54 (0.32-0.80) GtCO2/Mha) towards achieving PA Article 2 and KM-GBF Target 2. Within the funnel there is a ‘channel’ in which synergies are maximised. The funnel highlights the complex dynamics that can emerge from the interplay of climate and biodiversity and provides a simple filter to prioritize effective joint action.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".