Predator–prey interactions influenced by a dynamic river plume
Bibliographic record
Abstract
Marine predator–prey interactions are often influenced by oceanographic processes that aggregate prey. We examined density distributions of seabirds and prey fish associated with the Columbia River plume to determine whether variation in plume size (i.e., volume or surface area) or location influences predator–prey interactions. Common murre (Uria aalge), sooty shearwater (Ardenna grisea), and forage fish, including northern anchovy (Engraulis mordax) and juvenile salmon (Oncorhynchus spp.), occurred disproportionately in plume waters relative to adjacent marine waters. Water clarity, an indicator of plume-influenced waters, was a significant predictor of seabird and prey densities throughout the survey area. Murres occurred within 20 km of the plume center of gravity, whereas shearwaters occurred ∼100 km north of the plume center of gravity, concurrent with the highest densities of prey fish. Global indices of collocation were relatively low between murres and prey compared with the high values between shearwaters and prey. Seabird densities were negatively correlated with plume size, suggesting that seabirds concentrate in the plume to maximize foraging effort. We conclude that variation in Columbia River plume size and location influences predator distributions, which increases predation pressure on prey, including threatened salmonid species.
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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 | 0.001 | 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".