Spatial and temporal variance of walleye pollock (<i>Theragra chalcogramma</i>) in the eastern Bering Sea
Bibliographic record
Abstract
Mobile acoustic surveys attempt to map and count aquatic organisms without biasing abundance estimates. Horizontal and vertical movements by target species may influence density measurements and net samples during acoustic surveys. To investigate the influence of fish movement on density data, we compared temporal and spatial variability of walleye pollock (Theragra chalcogramma) in three sets (2 night, 1 day) of 14.8-km transects in the eastern Bering Sea. Walleye pollock density distributions were also compared with those in the five nearest daytime survey transects. We found that horizontal density distributions did not change at temporal scales ≤4 h and that spatial variance remained consistent at scales ≤2.5 km. Spatial variance density patterns were similar in transects sampled during the day compared with those sampled at night and were also similar in along-shore compared with cross-shore transects. Transects that contained two biological scattering layers could be vertically separated into zooplankton and fish. Spatial variance patterns in the upper zooplankton layer mimicked those of passive tracers, while patterns in the lower layer were consistent with those previously observed for mobile nekton. Current sampling resolution of acoustic surveys adequately captures horizontal spatial variance of walleye pollock in the Bering Sea.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".