A focus on Global Biodiversity Framework Target 7: Approaches to pollution
Bibliographic record
Abstract
A focus on Global Biodiversity Framework Target 7: Approaches to pollution Dr Christoph Neumann analyses the Global Biodiversity Framework Target 7, and explains the need for a qualified risk-based approach to pollution from the COP15 convention. The key outcome of the COP15 Convention on Biological Diversity (CBD) meeting in December 2023 in Montreal was adopting the Global Biodiversity Framework. Target 7 of the Global Biodiversity Framework, as adopted, reads as follows: “Reduce pollution risks and the negative impact of pollution from all sources, by 2030, to levels that are not harmful to biodiversity and ecosystem functions and services, considering cumulative effects, including:” (a) “reducing excess nutrients lost to the environment by at least half including through more efficient nutrient cycling and use;” (b) “reducing the overall risk from pesticides and highly hazardous chemicals by at least half including through integrated pest management, based on science, taking into account food security and livelihoods; and also” (c) “preventing, reducing, and working towards eliminating plastic pollution.” (1)
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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 | 0.020 | 0.013 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.017 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.022 | 0.024 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".