How does the accuracy of fisher knowledge affect seahorse conservation status?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it