Investigating the Effect of Oceanographic Conditions and Swimming Behaviours on the Movement of Particles in the Gulf of St. Lawrence Using an Individual-Based Numerical Model
Bibliographic record
Abstract
In this study an individual-based numerical model with three-dimensional (3D) and time-dependent fields of circulation and hydrography is used to examine the effects of the physical environment and various biological behaviours on the distribution and movement of particles in the Gulf of St. Lawrence and adjacent waters. The 3D circulation and hydrographic fields are simulated by a numerical ocean circulation model. The model domain covers the St. Lawrence Estuary (SLE), the Gulf of St. Lawrence (GSL), the Scotian Shelf, the Gulf of Maine, and their adjacent waters. The basis of the individual-based model is a numerical scheme that tracks the movement of particles carried by ocean currents. Several swimming behaviours of marine animals are considered with efficient seaward migration in the GSL as the goal. Electronic tagging data for the American eel (Anguilla rostrata) are used as guidance in specifying the behaviours. It is demonstrated that particles that undergo an observed behaviour, known as selective tidal stream transport, are able to exit the SLE more efficiently than particles that are carried passively by the 3D ocean currents. Outside the SLE, particles that search for and swim towards higher salinity move further downstream than those that have a preference for deeper water or swim in random directions.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".