Modelling the timing and duration of dormancy in populations of Calanus finmarchicus from the Northwest Atlantic shelf
Bibliographic record
Abstract
Calanus finmarchicus relies on dormancy to thrive in the seasonal environment of the boreal Atlantic. The lipid accumulation window (LAW) hypothesis proposes that a seasonal window of environmental conditions allows developing individuals to store enough lipids for dormancy to be safely initiated. Successful dormancy requires a sufficient amount of lipids to fulfil the reduced metabolic demand of the dormant individual and to sustain the final maturation process. We used a pattern-oriented modelling approach that implements the LAW hypothesis and employs a genetic algorithm for parameter estimation, in order to reproduce the observed phenology and demography of C. finmarchicus populations from the two contrasting regions, the Gulf of St. Lawrence (GSL) and the Gulf of Maine (GoM) in the northwest Atlantic shelf. In the GSL, the model reproduced the timing of dormancy, the abundance and individual condition of late copepodid stages. In the GoM, the model produced a semi-annual dormancy pattern, as no locally produced individual could last the 6–8 months of dormancy inferred from the available observations. Further testing requires extending demographic time series, including lipid condition of late copepodid stages in the GoM, and the implementation of a 3-D modelling framework that would explicitly address the complex interactions between circulation and population dynamics of C. finmarchicus over the entire northwest Atlantic shelf.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".