How does the accuracy of fisher knowledge affect seahorse conservation status?
Bibliographic record
Abstract
Abstract Despite a growing interest in incorporating fisher knowledge into quantitative conservation assessments, there remain practical impediments to its use. In particular, there is some debate about the accuracy of fisher knowledge. In this study, we report an attempt to quantify assumptions about how accurately fishers report past events (retrospective bias). Then we examine how the assumption we make about retrospective bias affects the characterization of changes in the fishery and extinction risk. We link fisher interviews and fisher logbooks to establish a catch rate (catch per unit of effort) trend for the history of a data‐poor, small‐scale seahorse fishery in the Philippines. We find that fishers perceive historic declines in fishing rate that are not apparent in more recent logbook trends, and the extent of the decline (and therefore extinction risk) hinges on assumptions we make about the accuracy of fisher recall. Scenarios that ignore retrospective bias result in the most severe declines and the most worrying extinction risk classifications. Furthermore, the historic baseline set by interviews suggests that relying on recent decades of data alone may underestimate extinction risk for our study species, and others that have been historically exploited. Attempting to link interviews with logbooks also illustrates differences between fisher‐derived datasets: retrospective interviews may exaggerate early fishing rates and capture less variability than logbooks. In addition to being the first seahorse fishery reconstruction, our work contributes to the emerging interest in how fisher knowledge can guide conservation assessment. Future studies that incorporate fisher knowledge into quantitative assessments require (1) clearly stated assumptions about fisher knowledge bias; (2) clear criteria to compare fisher knowledge collected with different methods; (3) evaluation of the impact of assumptions on assessments.
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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 | 0.025 | 0.170 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 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".