Physiological biomarkers and fisheries management
Bibliographic record
Abstract
Abstract The benefits of physiological biomarkers, knowledge and concepts are well-established in fish and wildlife management as they confer the ability to understand mechanistic processes, identify cause-and-effect relationships, and develop predictive models. Although this approach is gaining momentum in the context of species conservation, the use of physiological biomarkers in exploited marine fish stock management and recovery plans remains relatively rare. Here, we present five essential issues to consider to implement physiological biomarkers in fisheries management: (i) choice of relevant biomarkers that have a well-known mechanistic basis, (ii) identification of species-specific biomarkers reflecting a meaningful timespan for management, (iii) selection of biomarkers compatible with data collection during routine scientific fisheries surveys, (iv) use of biomarkers as early-warning signals and complementary indicators of population-level changes in life history traits and (v) how physiological biomarkers may help to refine long-term population dynamic projections under climate change and management scenarios. Overall, if based on well-established mechanisms linked to individuals’ fitness, a focus on physiological biomarkers should help to better understand the mechanisms behind stock declines, changes in stock characteristics, and thus more efficiently manage marine fisheries and conserve populations. As this approach is transferable among species, locations, and times, the integration of physiological biomarkers in fisheries science has the potential to more broadly enhance assessments and management of fish stocks.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".