Marine biodiversity offsets: Pragmatic approaches toward better conservation outcomes
Bibliographic record
Abstract
Abstract Increasing exploitation of marine natural resources and expansion of energy infrastructure, shipping, and aquaculture across the oceans are placing increased pressure on marine life. Biodiversity offsets, as the last stage of the mitigation hierarchy, provide an opportunity to promote a more sustainable basis for development by addressing residual impacts and achieving “no net loss” for biodiversity. Despite debate around their effectiveness, biodiversity offsets are seeing increasing application on land but remain a rarely used tool in the marine environment. We assess how offsets can be applied in the marine environment to achieve better biodiversity outcomes, and identify implications for conservation policy and practice. For instance, spatial conservation planning provides opportunities to move away from a siloed, project‐by‐project, approach by pooling offsets on a regional scale. There are real differences between marine and terrestrial environments in relation to ecology, connectivity, data availability, management options, and impact perception, and marine offsets are therefore often regarded as challenging. However, fundamental offset principles, types, and approaches apply equally on land and at sea. Marine biodiversity offset approaches can build on the experience of terrestrial offsets but can also innovate to help achieve biodiversity gains and contribute toward global and national biodiversity targets.
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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 | 0.044 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.016 |
| Scholarly communication | 0.013 | 0.011 |
| Open science | 0.002 | 0.018 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.018 | 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".