Larval trophodynamics, turbulence, and drift on Georges Bank: A sensitivity analysis of Cod and Haddock
Bibliographic record
Abstract
Using an individual-based model approach we consider trophodynamic effects on the growth and survival of larval cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) on Georges Bank during late winter/early spring. These studies represent an extension of results described in Werner et al. (1996; Deep-Sea Res. II), wherein the effect of turbulence-enhanced larval-prey contact rates increased the effective prey concentration resulting in growth of cod larvae consistent with observed rates in the field. We reformulated the feeding of the larvae to include existing relationships between maximum prey-length and larval-length and we examined: (i) larval search behaviour and its effect on encounter with prey, (ii) the ability of larvae to pursue and capture prey in a turbulent environment, and (iii) the effect of turbulence on the dispersion of larvae in the vertical. We find that search behaviour, the effect of turbulence on pursuit and capture, and vertical dispersion decrease the predicted larval growth rates compared to those observed in the earlier study. These results suggest that larval feeding behaviour, and especially the ability of larvae to pursue encountered prey, could be an important input to larval growth and survival models. The inclusion of turbulence in determining the position of passive larvae in the water column allows the larvae to sample the entire water column, contributing to a decrease in the variance of the size of the larvae over time. The ability of larvae to swim and aggregate in the vertical will be necessary to reproduce distributions observed in the field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".