Variability in trap catches for an American lobster, <i>Homarus americanus</i> , spring fishery
Bibliographic record
Abstract
Abstract At‐sea sampling is a common approach used by fisheries scientists to assess changes in fished populations. Traditional sampling programmes focus on short intensive sampling periods by fisheries personnel, although there has been a move to increase temporal sampling frequency within a fishing season by using harvesters. To determine the suitability of these two options, we compared the precision of estimates obtained for the American lobster (Homarus americanus) fishery in the southern Gulf of St. Lawrence, Canada. The sampling variance estimation for the mean catch‐per‐unit‐effort (CPUE) was based on a three‐stage sampling design with days as the primary unit, and buoy and trap as secondary and third stage units, respectively. Using the estimated variance components to predict and compare the variance of the mean CPUE for different at‐sea sampling designs, we show that it would be more efficient to sample a few traps (at least 3) every day for the entire fishing season than the traditional at‐sea sampling of the entire fishing gear twice or three times in a season by scientific personnel. Designing a harvester‐based at‐sea sampling programme could be an efficient approach for reducing costs while gathering essential fishery data, improving dialogue between the industry and scientists, and increasing harvesters’ participation in managing the resource.
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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 | 0.001 | 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 | 0.000 |
| 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 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".