Comparing Fisher Interviews, Logbooks, and Catch Landings Estimates of Extraction Rates in a Small-Scale Fishery
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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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 | 0.011 | 0.070 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".