Individual‐based modeling explains the contrasted seasonality in size, growth, and reproduction of the sympatric Arctic (<scp><i>Thysanoessa raschii</i></scp>) and Nordic krill (<scp><i>Meganyctiphanes norvegica</i></scp>) in the St. Lawrence Estuary, eastern Canada
Bibliographic record
Abstract
Abstract The Nordic krill Meganyctiphanes norvegica and Arctic krill Thysanoessa raschii both dominate the krill community within the Estuary and Gulf of St. Lawrence system where they are central forage species for its pelagic ecosystem. We developed a species‐specific physiological individual based model that implements the critical physiological processes of growth, molting, and reproduction of female adults as responses to environmental forcing. Key innovations of our approach were the decoupling between the molting schedule and growth, as well as considering two distinct sources of prey (phytoplankton and mesozooplankton). Our simulation results revealed that the details of the feeding process were critical for an accurate representation of the production dynamics of adult individuals from both species. Their specific feeding preferences on phytoplankton and mesozooplankton resulted in distinct species‐specific phenological patterns that reproduced observations. The present study highlights the importance of detailed knowledge of diet and feeding behavior of krill species to improve our understanding of population responses in a rapidly changing environment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".