Estimating recreational harvest using interview‐based recall survey: implication of recalling in weight or numbers
Bibliographic record
Abstract
Abstract For many overfished marine stocks, recreational fishing continues even though recovery plans are implemented and commercial landings regulated. In such cases, unbiased and precise estimates of recreational harvest are important for successful management. Harvest estimation often relies on interviewed‐based surveys where fishers are asked to recall harvest within a given timeframe. However, the importance of whether fishers are requested to provide figures in weight or number is unresolved. Therefore, a recall survey aiming at estimating recreational harvest was designed, such that respondents could report harvest using either weight or numbers. It was found that: (1) a preference for reporting in numbers dominated; (2) reported mean individual weight of fish caught, differed between units preferences; and (3) when an estimate of total harvest in weight are calculated, these difference could result in a substantial bias through the conversion from numbers to weight. Based upon these results it is recommended that recreational harvest should be requested in numbers and not weight.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".