Effects of predation from juvenile herring (<i>Clupea harengus</i>) on mortality rates of capelin (<i>Mallotus villosus</i>) larvae
Bibliographic record
Abstract
Predation has been suggested as a cause of substantial mortality of fish larvae to the degree that it might influence recruitment. This field-based study concludes that juvenile herring ( Clupea harengus ) as small pelagic predator can significantly affect mortality rates of the planktonic larvae of capelin ( Mallotus villosus ) in the Barents Sea. Surveys were carried out in the summers of 2001 and 2003. In 2001, juvenile herring were widely distributed and overlapped with capelin larvae over a wide area, whereas in 2003, the herring were more aggregated. The study focused on predation in the areas of predator–prey overlap. Capelin larvae were observed in the herring stomachs at 11 of 24 stations and at 8 of 16 stations where herring were caught in 2001 and 2003, respectively. At those stations, an estimated 7.3% and 9.9% of the capelin larvae were eaten by herring per day in 2001 and 2003, respectively. Statistical models revealed that density of capelin larvae and copepods and occurrence of euphausiids in the stomachs affected the number of capelin larvae per predator stomach. A simplified model with only capelin larvae density as predictor was converted to a functional response relationship using an experimentally derived digestion rate estimate for capelin larvae in herring stomachs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".