A stakeholder-engaged approach to evaluating spawning aggregation management as a strategy for conserving bonefish (Albula vulpes) in Cuba
Bibliographic record
Abstract
spp.) species aggregate to spawn and are culturally and economically important, but generally lack management such as spawning area protections to ensure that fisheries are sustainable. Here, we use Cuba as a case study to inform the development and refinement of management strategies for bonefish. Recommendations for the management of bonefish pre-spawning aggregations were based on international experiences, which have been adapted to the Cuban context from results of surveys and interviews with Cuban fisheries professionals and fishing guides. The achievability and feasibility of recommendations were further reviewed by additional experts in the field of fisheries, management and Cuban policy. The process revealed extensive data-limitations for bonefish fisheries and underscored the importance of including fishing guides, local ecological knowledge and the context of marine protected areas in Cuba for bonefish management. Recommendations include (1) initiating information exchange between Cuban management agencies and third-party institutions related to bonefish management; (2) utilizing local ecological knowledge to gather information, formulate management strategies and enforce regulations; (3) implementing spatial and temporal management measures for bonefish spawning sites; (4) using what is already in place, by protecting spawning sites in the context of existing marine protected areas; (5) collaborating with all stakeholders to manage bonefish spawning sites; and (6) reducing the commercial harvest of the species. Supplementary Information: The online version contains supplementary material available at 10.1007/s10641-022-01355-0.
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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 | 0.016 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".