Environmental rejuvenation of the Gulf by compensation and restoration
Bibliographic record
Abstract
The Gulf is considered to be a young sea in decline, with poor prognosis for continuing production of abundant natural resources. We compare and contrast ‘monetary’ and ‘environmental’ compensation as mechanisms for addressing ecosystem damage in the Gulf. The 1992 International Oil Pollution Compensation Conventions settle claims financially, but only for certain categories of oil spills. For example, aside from inherent difficulties of valuing ecosystem services and their losses, ecological damage from the time of injury to recovery (interim losses) is not compensated. Another approach involves reimbursement for environmental action/projects to restore affected resources and offset impacts until recovery. In habitat equivalency analysis, mitigation requirements are calculated from the type(s), severity, duration and extent of resource impacts. This approach was utilized to resolve several claims for damage from the 1991 Gulf War oil spill. Various compensatory projects resulted, including direct oil spill remediation and other environmental projects such as the establishment of ≥1 protected area (x ha for y years). Besides compensation, in this paper we advocate setting threshold levels for the protection of different coastal and marine ecosystems. This could be achieved by a proportion (c. 30%) of every major ecosystem becoming fully protected, through an expanded regional protected area network.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".