Gaining Traction: Retreading the Wheels of Marine Conservation
Bibliographic record
Abstract
A number of international treaties address the conservation of marine resources. The declining state of the world's oceans suggests that these treaties are not succeeding and could use improvement. The Convention on International Trade in Endangered Species (CITES) is increasingly embracing the conservation of marine species. We examine the evolution of marine species protection under CITES and illuminate some of the mechanisms used and challenges faced in implementing CITES protection. We found that clarification is needed on when and where CITES applies and how CITES should work with other treaties and institutions. The Society for Conservation Biology (SCB) can contribute to increased effectiveness of CITES for marine conservation. Foremost, the SCB community could foster dialogue on creating a broad vision of how CITES should apply to marine species and how it can synergistically interact with other important marine-conservation treaties and institutions. More specific contributions could focus on defining listing criteria for marine species, improving the science behind the nondetriment finding, and offering technical guidance on species proposals. A future role for SCB could be to contribute to the enhanced effectiveness of other marine conservation agreements such as the Convention on the Conservation of Migratory Species of Wild Animals, the International Whaling Commission, and the United Nations Convention on the Law of the Sea.
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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 | 0.037 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.014 | 0.063 |
| Scholarly communication | 0.026 | 0.046 |
| Open science | 0.004 | 0.023 |
| Research integrity | 0.017 | 0.023 |
| Insufficient payload (model declined to judge) | 0.031 | 0.006 |
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".