Introducing selfisher: open source software for statistical analyses of fishing gear selectivity
Bibliographic record
Abstract
There is a need to improve fishing methods to select for certain sizes and species while excluding others. Experiments are conducted to quantify selectivity of fishing gears and how variables such as gear design (e.g., mesh size, mesh shape), environmental parameters (e.g., light, turbidity, substrate) or biological parameters (e.g., fish condition) alter selectivity; the resulting data need to be analyzed using specialized statistical methods in many cases. Here, we present a new tool for analyzing this type of data: an R package named “selfisher”. It allows estimating multiple fixed effects (e.g., fish length, total catch weight, environmental variables) and random effects (e.g., haul). A bootstrapping procedure is also provided. We demonstrate its use via four case studies, including (A) covered codend analyses of four gears, (B) a paired gear study with numerous covariates, (C) a catch comparison study of unpaired hauls of gillnets and (D) a catch comparison study of paired hauls using polynomials and splines. This software will make it easier to model selectivity, teach statistical methods, and make analyses more repeatable.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.033 |
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".