Factors for the variability of discards: assumptions and field evidence
Bibliographic record
Abstract
Discarding is an issue of increasing concern and there is a growing number of studies aiming at estimating discard amounts and characteristics. However, the sampling design and methods used in these studies generally rely on implicit assumptions. In this perspective, we examine the available evidence in favour of or refuting these assumptions. We find that (i) the assumptions most commonly used for estimating discards, namely that discards are proportional to catch or to effort, are generally not supported by the available evidence, (ii) both environmental conditions and fishing methods influence the amounts and composition of discards, but because of the huge variability, sampling stratification according to these factors might not result in any improvement of the precision of discard estimates, and (iii) many intricate factors can play a role in determining discards in a particular fishery. We conclude that assumptions should be more carefully checked prior to being taken for granted in discard studies and that more studies designed to improve knowledge of the discarding processes are needed.
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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 | 0.165 | 0.427 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".