Can the data from at-sea observer surveys be used to make general inferences about catch composition and discards?
Bibliographic record
Abstract
Some fishery characteristics such as total discards are often inferred from data collected by at-sea observers on a subset of fishing trips. Such inference is predicated on the assumption that observed and unobserved trips are statistically exchangeable. There are two principal reasons why this may not be so. A deployment effect results from nonrandom distribution of observers among sampling units. An observer effect results from changes in fishing practice or location when observers are present. Both effects can impact the precision and accuracy of fishery-level inferences drawn from observer data, though this is rarely addressed quantitatively. We found evidence for deployment and observer effects in Gulf of St. Lawrence fisheries. The impact of deployment bias was further quantified by resampling from fisheries data collected with 100% observer coverage. We conclude that the nature of the effects observed in our study preclude merely correcting observer-collected catch data for possible biases and imprecision. Furthermore, regulatory compliance monitoring by observers in the existing program may not be completely effective. Modifications to program structure would therefore be beneficial and some suggestions are evaluated in this paper.
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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 | 0.179 | 0.464 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.006 | 0.017 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".