Kunming-Montreal Global Biodiversity Framework: Challenge and Future Options to Address Anthropogenic Underwater Noise
Bibliographic record
Abstract
Abstract The ocean, which plays an essential role in supporting human life, continues to deteriorate due to anthropogenic underwater noise. This unseen form of pollution is a significant emergent threat to marine and coastal biodiversity. Substantial discussions have occurred on the problem under the Convention on Biological Diversity ( cbd ) yet further action is needed. The adoption of the Kunming-Montreal Global Biodiversity Framework ( gbf ) in 2022 provides a potential partial solution to address the problem. This article reviews the current development of the gbf concerning the protection of the marine environment, particularly from the threat of anthropogenic underwater noise. It is argued that the gbf , along with its monitoring framework, could provide an opportunity to strengthen further action for the conservation of marine and coastal biodiversity against this problem. The importance of capacity-building to ensure that developing countries have the capacity to address anthropogenic underwater noise is emphasized.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".