Guidelines for Evaluating Artificial Light to Mitigate Unwanted Fisheries Bycatch
Bibliographic record
Abstract
Evaluating artificial light as a bycatch reduction device (bycatch reduction light, “BRL”) requires a multidisciplinary approach that applies knowledge of fisheries science, fishing technology, engineering, physics, optics, vision biology, oceanography, animal behavior, economics, and social science. To support the continued evaluation of BRL, these guidelines were developed for conducting standardized and systematic studies. The guidelines highlight how information from those fields of study contributes to the efficacy of study design and the evaluation of results. The guidance is focused on four core areas: (i) defining the objective of using a BRL; (ii) understanding the context in which the BRL is applied and considering the base knowledge that is needed; (iii) selecting an appropriate study design (including selection and placement of the BRL) and analytical methods for measuring both behavioral responses and catch outcomes from using the BRL; and (iv) interpreting the data through the lens of the base knowledge, context, and study design, and evaluating the results against an established definition of success and variables that affect adoption. The purpose of these guidelines is to increase the ability of researchers and managers to determine if BRL is appropriate for a fishery and to encourage consistency in data collection among studies to support future meta-analyses and inter-study comparison. In addition, suggestions are provided on where more research and technology development are needed to support this rapidly emerging field of research.
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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 | 0.210 | 0.293 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.017 | 0.010 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.014 | 0.006 |
| Research integrity | 0.016 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.006 |
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".