Modelling feeding, growth, and habitat selection in larval Atlantic cod (<i>Gadus morhua</i>): observations and model predictions in a macrocosm environment
Bibliographic record
Abstract
Individual-based models (IBMs) integrate behavioural, physiological, and developmental features and differences among individuals. Building on previous process-based models, we developed an IBM of larval Atlantic cod (Gadus morhua) that included foraging, size-, temperature-, and food-limited growth, and environmental factors such as prey-field, turbulence, and light. Direct comparison between larval fish IBMs and experimental studies is lacking. Using data from a macrocosm study on growth and feeding of larval cod, we forced the model with observed temperature and prey-field and compared model predictions with observed distribution, diet, size-at-age, and specific growth rates. We explored implications of habitat selection rules on predicted growth rates. We analyze the sensitivity of model predictions by the Latin Hypercube Sampling method and individual parameter perturbation. Food limitation prevented larvae from growing at their physiological maximum, especially in the period 5–17 days post hatch (DPH). Active habitat selection had the potential to enhance larval growth rates. The model predicted temperature-limited growth rates for first-feeding larvae (5–20 DPH) when prey density is >5 nauplii·L–1. After age 20 DPH, maximum modelled growth required a diet of copepodites. Simulated growth rates were close to observed values except for the period just after the start of exogenous feeding when prey density was low.
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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 | 0.001 | 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".