Measuring progress toward global marine conservation targets
Bibliographic record
Abstract
Marine species and their habitats are facing widespread overexploitation and degradation, respectively. In response to urgent calls for their protection, the international community agreed to establish representative networks of marine protected areas by 2012 that would conserve and protect 10–30% of specific habitats. To achieve these goals will require reliable estimates of the total area occupied by each habitat. We evaluated this assumption for coral reefs by generating estimates of coral reef area from high‐spatial‐resolution, remotely sensed imagery (30‐m resolution Landsat data), and comparing these with existing published data (usually >1‐km resolution). Discrepancies between previous estimates and our values ranged from +1316% to −64%. This uncertainty is incompatible with realistic achievement of the 10–30% conservation targets. We conclude that currently available estimates of the global extent of most coastal marine habitats are based on data that are too poorly resolved to be useful in evaluating progress toward the 2012 targets. Most countries will therefore be unable to demonstrate that they have fulfilled their commitments to marine biodiversity conservation. We urge that accurate inventories be conducted, in a cost‐effective fashion, through analyses of available high‐spatial‐resolution satellite imagery.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".