Numerical Investigation of Diapycnal Mixing of the Kitikmeot Sea in the Southern Canadian Arctic Archipelago
Bibliographic record
Abstract
Utilizing a 1/12∘ numerical model, we examine the diapycnal mixing patterns in the Kitikmeot Sea, a semi-enclosed water body within the southern Canadian Arctic Archipelago. The analysis reveals that mixing intensity near the sea surface varies seasonally, with effective diffusivity values ranging from 10−5 to 10−3m2s−1, while away from the surface, the effective diffusivity remains relatively stable between 10−5 and 10−4m2s−1. The seasonal fluctuations in surface mixing intensity are strongly influenced by ice coverage, which impacts both the stratification and the energy input to the surface driving the mixing process. Mixing energetics analysis indicates that the majority of energy contributing to the mixing processes are applied to the sea surface. During ice-free periods, wind-driven stirring dominates near-surface mixing with effective diffusivities of 10−5 to 10−4m2s−1. Minimum near-surface effective diffusivity values occur in July and August, when the surface water is fresher and near-surface stratification is stronger due to spring freshet. Conversely, during ice-covered seasons, surface cooling and brine rejection primarily drive near-surface mixing, leading to effective diffusivities of 10−3m2s−1 or higher. In most cases, the observed mixing efficiency is within the range of what has been found in other regions of the Arctic Ocean.
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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 | 0.001 | 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".