Comparing Fisher Interviews, Logbooks, and Catch Landings Estimates of Extraction Rates in a Small-Scale Fishery
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
Researchers are turning to alternative data sources (e.g., resource user knowledge) to provide information required for wildlife management. Little is known about the reliability of data elicited from resource users relative to data obtained from user-independent approaches (e.g., observations of fish catches). We test for consensus among three methods that quantify past (1996 to 2007) seahorse catch-per-unit-effort (CPUE) for a small-scale, data-poor fishery in the Philippines: interviews with fishers about good, bad, and typical catch; fisher logbooks; and observations of catch landings. Interviews and logbooks indicated no trends in CPUE through time, consistent with results from the fisher-independent metric, catch landings. Although interview estimates of “typical” CPUE greatly exceeded “typical” observed catches and logbook estimates, interview estimates of “bad” CPUE were comparable. Catch landings estimates for a fisher in a particular year were uncorrelated to what he reported during retrospective interviews. Interviews should be used cautiously to inform specific catch targets (e.g., total allowable catches), although including interview questions about a range of catch experiences (e.g., good, bad and typical), may improve interview-derived data. Logbooks are particularly useful for capturing information about fishing expeditions that produce no fish, which are largely missed by other methods.
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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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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