Japan shellfish farmer perceptions of ocean acidification, adaptive strategies and comparison with global shellfish farmers
Bibliographic record
Abstract
Ocean acidification (OA) poses significant threats to shellfish aquaculture. Although governments and organizations around the globe are taking actions to mitigate the impacts of OA, few studies directly report shellfish farmer perceptions of OA and corresponding responses. In this study, we document Japanese shellfish (oyster) commercial farmer perceptions of, and adaptive strategies for OA with respect to oyster aquaculture. We also review and compare our results with existing studies of shellfish commercial farmer perceptions of OA in three regions, including the United States (U.S.), the Mediterranean region and British Columbia, Canada. We found variation in the perceptions of OA around the globe; it is common among all shellfish farmers to have difficulty distinguishing OA from other environmental stressors. OA adaptive strategies from shellfish farmers were only reported for the U.S. (in the literature), and Japan (this study). Acknowledging the diverse geographical and cultural backgrounds, we discussed the similarity and difference of adaptive strategies between the U.S. (as a post-event case with documented OA-related shellfish mortality) and Japan (as a pre-event case) to cope with OA. For example, farmers from both countries suggest, or are already utilizing flexibility in farm management and applying knowledge through hands-on learning. While U.S. farmers rely on networking with different stakeholders to learn about OA knowledge and solutions while Japanese farmers do not. Learning from the strategies that U.S. farmers applied to adapt to OA events, several areas of policies and actions (e.g., financial support, collaboration with scientists and OA awareness enhancement) were identified to better support and empower Japanese shellfish farmers to adapt to future OA scenarios. However, future study on suitability and transferability of implementing policies and actions in Japan is required due to different geographical and cultural contexts.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".