Beach and demersal spawning in capelin (<i>Mallotus villosus</i>) on the northeast Newfoundland coast: egg developmental rates and mortality
Bibliographic record
Abstract
Timing of spawning and site selection in fish are important fitness-related traits that ultimately influence reproductive success. Capelin ( Mallotus villosus (Müller, 1776)), a key forage fish in the north Atlantic, spawn eggs that adhere to sediments on beaches and in demersal (deep-water) habitats throughout their geographic range, resulting in divergent thermal regimes for the incubating eggs. We compare the timing and duration of spawning of capelin and its influence on the developmental and survival rates of eggs between a beach and a demersal spawning site on the northeast Newfoundland coast in 2004 and 2005. Spawning events at the beach were at least 10 days earlier and shorter (2–3 days) relative to the demersal site (8–12 days). Hourly and mean daily incubation temperatures at the beach were higher and more variable relative to the demersal site, resulting in two distinct developmental strategies: low to high mortality and rapid development (beach) versus low mortality and slow development (demersal). Higher egg mortality at beach sites was explained by higher and more variable temperatures and potentially limited oxygen replenishment relative to demersal sites. The divergent biology of beach and demersally spawned eggs suggest that each will respond differently to environmental change and, thus, require different approaches for successful management.
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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 | 0.001 | 0.001 |
| 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".