A general methodology for the estimation of gillnet size-selectivity and population length frequencies
Bibliographic record
Abstract
Abstract The size selectivity of gillnets can be estimated using the catch data from experimental fishing of gangs of net panels having different mesh sizes. Gillnet selectivity curves can take a wide variety of shapes, and currently their estimation requires consideration of several different parametric forms, with right-skewed or bimodal curves typically being preferred. Here it is shown that the generalized additive model (GAM) framework provides a convenient and more flexible alternative. The GAM approach also generalizes the scope of analysis by permitting the population length frequencies of encounter to be jointly estimated as a smooth function of length. Moreover, the GAM framework allows for the inclusion of covariates such as sex or a condition index, and accommodates hierarchical sampling designs and spatial or temporal effects. Relative fishing power of the different sized meshes can also be included, notwithstanding that care with non-identifiability is required. The ease of use of the GAM approach is demonstrated on previously published lake trout data.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".