Isopycnal deepening of an under‐ice river plume in coastal waters: Field observations and modeling
Bibliographic record
Abstract
The Great Whale River, located on the southeast coast of Hudson Bay in Canada, forms a large river plume under complete landfast ice during early spring. Short‐term fluctuations of plume depth have motivated the present numerical study of an under‐ice river plume subject to tidal motion and friction. We introduce a simple two‐layer model for predicting the vertical penetration of the under‐ice river plume as it propagates over a deepening topography. The topography is idealized but representative. Friction on the bottom surface of the ice cover, on the seabed, and at the plume interface is parameterized using the quadratic friction law. The extent of the vertical penetration is controlled by dimensionless parameters related to tidal motion and river outflow. Model predictions are shown to compare favorably with under‐ice plume measurements from the river mouth. This study illustrates that isopycnal deepening occurs when the ice‐cover vertical motion creates a reduced flow cross‐section during the ebbing tide. This results in supercritical flow and triggers the downward plume penetration in the offshore. For a given river discharge, the freshwater source over a tidal cycle is unsteady in terms of discharge velocity because of the variation in the effective cross‐sectional area at the river mouth, through which freshwater flows.
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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 | 0.000 |
| 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 | 0.000 | 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 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".