An optimisation model of the diel vertical migration of northern krill (<i>Meganyctiphanes norvegica</i>) in the Clyde Sea and the Kattegat
Bibliographic record
Abstract
An optimisation model was developed to examine the effect of predation risk and environmental conditions on the diel vertical migration (DVM) of adult northern krill (Meganyctiphanes norvegica). Model predictions were compared in two locations with contrasting environmental conditions, the Clyde Sea and the Kattegat. The model was constructed from a combination of parameterised functions and empirical field data obtained during summer conditions. Parameter matrices were set up to cover the entire water column over a 24-h period. The first matrix contained values for "net energy gain," which incorporated empirical data on temperature-dependent respiration, copepod and phytoplankton abundance, and a functional response model for feeding rate. The second matrix expressed the risk of encountering a generalised visual (fish) predator as a function of light levels. The optimisation procedure sought a path through depth and time such that the energy gain was equal to the amount necessary to grow, produce eggs, and moult, while the risk of predation was minimised. The model predicted DVM in both the Clyde Sea and the Kattegat. Sensitivity analyses showed that the predicted DVM pattern was mainly driven by food and predation risk, with temperature effects on metabolic costs having a minor effect.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".