Diurnal variation in bottom trawl survey catches: does it pay to adjust?
Bibliographic record
Abstract
The diurnal bias of bottom trawl survey catches is studied with the purpose of adjusting for it and thereby improving the accuracy of abundance estimates. The correction term is estimated with uncertainty and thus increases the variance of the resulting abundance estimate. To investigate this adequately, we use a stochastic model describing diurnal fluctuations and examine the annual variation of the diurnal amplitude as a function of species and length. The diurnal amplitude is fairly stable for large fish, and for these, the bias-corrected estimate leads to a moderate increase in variance. For small fish, the diurnal amplitude is unstable, however, and the correction of diurnal bias occurs at the expense of a large increase in variance. This unstable amplitude also leads to a large year-to-year variation in catchability for small fish. For haddock (Melanogrammus aeglefinus), the diurnal amplitude depends heavily on fish length, indicating a strong decrease in catchability with decreasing fish length.
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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 | 0.006 | 0.075 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".