Do survey design and wind conditions influence survey indices?
Bibliographic record
Abstract
Survey indices play an important role in stock assessments as they provide information on stock trends. In certain cases, large interannual variations have been observed that are unlikely to reflect true underlying stock changes but are rather outliers. When survey indices for several species appear to be outliers for the same year, the suspicion is raised that something happened during the survey of that year. This is called a year effect in survey catches. To study the potential year effect in survey catches for the French autumn groundfish survey taking place in the Bay of Biscay, several indicators for survey design and wind conditions were derived as explanatory variables, and principal component analysis was used to study the relationship between these variables. Using multiple linear regression models, we found that, on average, 20% of interannual variation in abundance indices could be explained by survey conditions for benthic species, 11% for demersal, and none for pelagic species. In contrast, survey conditions explained a smaller and decreasing part of the interannual variability in the coefficients of variations of these abundance indices and in species mean weight for benthic, demersal, and pelagic species. Thus survey indices of benthic species seemed most affected by survey design and wind conditions.
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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 | 0.033 | 0.109 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".