Estimating the Size Selectivity of Trap Nets using a Gill-Net Selectivity Experiment: Method Development and Application to Lake Whitefish in Lake Huron
Bibliographic record
Abstract
Abstract Fisheries management requires information about the size selectivity of the fishing gears, but often the experimental data to estimate size selectivity are absent. We developed a two-stage method to use data from an experimental survey for one type of gear (gill nets) to estimate the size selectivity of another type of gear (trap nets) based on the commercial catch data from both gear types, and we applied this method to Lake Whitefish Coregonus clupeaformis in Lake Huron. In stage I, an information-theoretic approach (Akaike's information criterion) was used to select among five candidate models for the size selectivity of experimental gill nets. A double logistic function (a dome-shaped curve skewed to the right) was chosen as the best model to describe the size selectivity of gill nets targeting Lake Whitefish. In stage II, we estimated the size selectivity of trap nets by applying the model of size selectivity from experimental gill nets to commercial catch data from trap-net and gill-net fisheries operating in a similar region and temporal period. Trap-net selectivity was fitted by using a symmetric logistic equation. This two-stage method predicted that 513-mm FL Lake Whitefish are fully selected by gill nets with a mesh size of 114 mm, whereas ≥615-mm FL fish are fully selected by commercial trap nets. Received April 12, 2017; accepted September 8, 2017 Published online November 9, 2017
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".