Less Specific and More Comprehensive? An Analysis of How the Ocean Is Reflected in the Kunming-Montreal Global Biodiversity Framework
Bibliographic record
Abstract
Abstract In response to the increasing trend of biodiversity decline globally and its consequences for the planet as a whole, the Convention on Biological Diversity Conference of the Parties adopted the Kunming-Montreal Global Biodiversity Framework (GBF) in December 2022. The Framework sets conservation, sustainability and equitable sharing of benefits from the use of genetic resources and MEAns of implementation goals to be achieved by 2050, and actionable targets to be achieved by 2030 to halt and reverse biodiversity loss. During the negotiations and even after the adoption of the GBF, much debate has surrounded how the ocean has been represented in the Framework. By addressing the scope of the GBF vis-à-vis the Convention, quantifying and comparing the use of marine-related terms in the GBF and in the previous biodiversity framework, and aligning relevant COP decisions and objectives and activities of the CBD Programme of Work on Marine and Coastal Biodiversity with the GBF targets, we conclude that the GBF does not fail to address marine and coastal biodiversity and all targets are applicable to this ecosystem.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.005 | 0.016 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".