Change-in-ratio estimates of lobster exploitation rate using sampling concurrent with fishing
Bibliographic record
Abstract
We present a continuous change-in-ratio (CIR) method for estimating lobster exploitation rate using data from monitoring traps continuously sampled during fishing. The exploitation rate is estimated by fitting a nonlinear model to ratios of exploited catch over total catch (exploited plus an unexploited reference class) as a function of the cumulative exploited catch. Confidence intervals are obtained by bootstrapping. The method is applied to data collected by nearly 100 lobster fishers who sampled monitoring traps in fishing areas of Nova Scotia, Canada, from 1999 to 2001, and to simulated data. Best estimates are obtained where the exploited and the reference length classes are adjacent and narrow. A method to predict the impact of season length restriction on exploitation rate is presented. Simulations demonstrate that the method displays some robustness relative to departures from the model's assumptions. Exploitation rate estimates decline for length classes in which the minimum legal carapace length has been increased. The continuous CIR method can provide daily, local, and length-specific estimates of exploitation rate. For similar sample sizes, continuous CIR estimates are better than CIR estimates based on pre- and post-season sampling. A continuous CIR method is cost efficient because the data can be collected during regular fishing activity.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".