Targeting ocean conservation outcomes through threat reduction
Bibliographic record
Abstract
Abstract Nations have committed to reductions in the global rate of species extinctions through the Sustainable Development Goals 14 and 15, for ocean and terrestrial species, respectively. Biodiversity loss is worsening despite rapid growth in the number and extent of protected areas, both at sea and on land. Resolving this requires targeting the locations and actions that will deliver positive conservation outcomes for biodiversity. The Species Threat Abatement and Restoration (STAR) metric, developed by a consortium of experts, quantifies the contributions that abating threats and restoring habitats in specific places offer towards reducing extinction risk based on the IUCN Red List of Threatened SpeciesTM. STAR is now recommended as an appropriate metric by recent disclosure frameworks for companies to report their impacts on nature and STAR has seen widespread uptake within the private sector. However, it is currently only available for the terrestrial realm. We extend the coverage of the threat abatement component of the STAR metric (START), used to identify locations where positive interventions could make a large contribution to reducing global species extinction risk and where developments that increase threats to species should be mitigated, to the marine realm for 1646 marine species. Reducing unsustainable fishing provides the greatest opportunity to lower species extinction risk, comprising 43% of the marine STARTscore. Three-quarters (75%) of the global marine STARTscore falls entirely outside the boundaries of protected areas and only 2.7% falls within no-take protected areas. The STAR metric can be used both to guide protected area expansion and to target other actions, such as establishment and enforcement of fishing limits, to recover biodiversity.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".