Patterns of larval emergence of capelin (Mallotus villosus) and environmental cues at demersal spawning sites on the northeastern coast of Newfoundland
Bibliographic record
Abstract
At spawning beaches in Newfoundland, Canada, capelin ( Mallotus villosus ) larval emergence is triggered by coastal water mass replacement (CWMR). CWMR causes sharp but short increases in temperature in coastal areas by the transport of warm surface water to the coast during onshore wind events. Warm water temperatures stimulate the active emergence of capelin larvae into predator-poor and prey-rich conditions favourable for larval survival. We investigated whether wind events influenced (i) the thermal properties of the water column and (ii) the larval emergence patterns at five demersal (deep-water) spawning sites on the northeastern coast of Newfoundland during the posthatch periods in 2004–2006. Winds oriented along the shore caused frequent and sharp increases in bottom temperature at all sites but did not appear to stimulate the release of larvae into the water column. Instead, peaks in larval densities corresponded to predicted peaks in hatching. Because of this and the presence of recently hatched larvae (3–5 mm) primarily in good condition, we hypothesize that larvae simply emerged from the sediment upon hatching at these demersal spawning sites. We conclude that patterns of larval emergence at demersal sites in this region are different from those reported for the beach in other regions of Newfoundland.
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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) | 0.001 | 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".