Protect kelp forests
Bibliographic record
Abstract
Kelp forests support biodiversity, human livelihoods, and essential ecosystem services along 30% of the world’s coasts, but they are under threat from marine heatwaves, harvesting, pollution, and overfishing (1). Despite increased advocacy for their global protection, including the International Union for Conservation of Nature Seaweed Specialist Group (2) and the Kelp Forest Challenge (3), the social and ecological losses from kelp forest degradation continue to grow (4). Political action will be required at national and international levels to coordinate and implement strategic, integrated, tangible protection measures for kelp forests globally (5). Most countries have committed to the Kunming-Montreal Global Biodiversity Framework and pledged to effectively protect and manage 30% of marine ecosystems by 2030 (6), particularly those critical for biodiversity. However, only 2.9% of the ocean is currently inside fully protected Marine Protected Areas (MPAs) (7), which are the most effective tool for biodiversity conservation (7) and climate resilience (8). Moreover, the framework does not specify which ecosystems should be prioritized.About 35% of floating kelp forests are located in the waters of Latin American countries (9), which remain far from meeting the 2030 targets. Mexico has lost more than 50% of its kelp forests as a result of recent marine heatwaves (10). Chile and Peru have witnessed large-scale degradation from direct extraction (11), leading to drastic biodiversity loss.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".