Effects of turbidity on the reactive distance, search time, and foraging success of juvenile Atlantic cod (<i>Gadus morhua</i>)
Bibliographic record
Abstract
We investigated the effects of turbidity on the foraging behaviour of juvenile Atlantic cod (Gadus morhua) on mysid prey (Praunus neglectus) in the laboratory. The influence of turbidity on vision and chemoreception was examined by measuring reactive distances and search times to visual, chemical, and visualchemical prey cues over turbidity levels ranging from 0.4 to 17·m1(beam attenuation·m1). We also compared foraging rates of juvenile cod on mysids in highly turbid water and clear water under well lit and totally dark conditions. Juvenile cod using chemical cues were able to locate mysids from significantly longer distances than when only visual cues were available. Turbidity did not affect reactive distance to chemical cues, and had only a weak negative effect on reactive distance to visual and visualchemical cues. Search time was variable, but tended to increase with turbidity. Turbidity did not affect predation rates on free-ranging mysids, but predation rates were significantly lower in dark conditions than in well lit conditions. We suggest that juvenile cod use chemoreception in conjunction with vision (at close ranges) to locate prey in highly turbid water.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.001 | 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".