The interactive effects of temperature and food consumption on growth of larval Arctic cod (<i>Boreogadus saida</i>)
Bibliographic record
Abstract
Understanding larval growth, mediated by the interaction of early life traits and environmental conditions, is crucial to elucidate population dynamics. We used a bioenergetic model as an integrative tool to simulate the growth of Arctic cod (Boreogadus saida) larvae and to test the sensitivity of modeled growth to temperature and food quantity and quality. The growth was computed as the energy gained through food consumption minus the energy lost through respiration and other metabolic processes. We extended a previously published bioenergetic model to cover the full range of larval length and used a simplified feeding module. This simplification allowed us to build a predictive tool that can be applied to larval Arctic cod at a large spatial scale. Our model suggested that with subzero temperatures in the High Arctic, larvae need to increase food consumption in order to reach the observed length-at-age in late summer. The modeled growth agreed well with the field observations in the High Arctic but was 2–3 times higher than the laboratory-derived growth rate, probably due to differences in food type and selective mortality. Our study reveals important knowledge gaps in our understanding of larval cod growth in the High Arctic, including the lack of empirical estimations of daily ration and respiration for larvae under the natural habitat temperatures.
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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 | 0.000 |
| 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) | 0.000 | 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".