Predation risk on larval gadids by pelagic fish in the Georges Bank ecosystem. I. Spatial overlap associated with hydrographic features
Bibliographic record
Abstract
We investigated the relationship between hydrographic variables on Georges Bank and the spatial distribution of larval Atlantic cod (Gadus morhua), larval haddock (Melanogrammus aeglefinus), and two planktivorous fish, Atlantic herring (Clupea harengus) and Atlantic mackerel (Scomber scombrus). We conducted cruises during April-May of 1990, 1994, and 1995 to document spatial distributions and employed logistic regression and a spatial-overlap index to assess relationships between hydrographic variables, spatial distributions and spatial overlap between gadid larvae and planktivorous fish. Cod larvae were more abundant in shallow well-mixed areas, while both haddock larvae and herring were more abundant in stratified areas. Atlantic mackerel occurred in deeper water and had little spatial overlap with the other species. In 1995, the presence of Scotian Shelf water and an intrusion of continental slope water altered temperature and salinity distributions and increased spatial overlap between cod larvae and herring. The stratified areas offer a higher concentration of prey for larval haddock, however there is increased exposure to planktivorous fish predators, creating a potential trade-off between predation and starvation. Due to the high abundance of planktivorous fish and high spatial and temporal overlap, predation is likely an important factor influencing survival of gadid larvae on Georges Bank.
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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 | 0.000 |
| 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 | 0.001 |
| 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".