The effect of light intensity on the availability of walleye pollock (Theragra chalcogramma) to bottom trawl and acoustic surveys
Bibliographic record
Abstract
Quantitative assessment of semidemersal fish such as walleye pollock ( Theragra chalcogramma ) is difficult because the proportion of walleye pollock available to standardized surveys varies temporally and spatially. The US National Marine Fisheries Service’s Alaska Fisheries Science Center conducts bottom trawl (BT) surveys to estimate the demersal portion of the walleye pollock population and acoustic trawl (AT) surveys to estimate the pelagic portion. Both surveys are conducted during daylight hours to minimize variability due to diel changes in vertical distribution. To test if daytime near-bottom light intensity affects the proportion of walleye pollock available to the BT survey, we concurrently measured light and walleye pollock abundance on the Bering Sea shelf. Logistic regression models demonstrated that both light and depth affected walleye pollock abundance estimates by either BT or AT surveys, with more walleye pollock available to the BT survey under high illumination and at shallow depths and less walleye pollock available to the AT survey under these conditions. This finding suggests that daytime survey catchability for walleye pollock depends on depth and light intensity and that incorporation of light measurements could improve the precision of abundance estimates of semidemersal species such as walleye pollock.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".