Numerical Investigation of Diapycnal Mixing in the Kitikmeot Sea, Canadian Arctic Archipelago
Bibliographic record
Abstract
The Kitikmeot Sea, located in the southern Canadian Arctic Archipelago, has particular features distinguishing it from the northern parts of the Archipelago. Substantial ice-free period, massive freshwater input from rivers, limited water exchange due to its surrounding narrow straits and shallow sills, can influence the local ocean dynamics, in particular, the mixing and transport in this sea. In this thesis, diapycnal mixing is investigated by analyzing the output data from a numerical simulation of the Kitikmeot Sea, with 1/12◦ horizontal resolution, during years 2003 to 2019. Mixing strength has been quantified in terms of diapycnal diffusivity values derived from a volume-averaged advection-diffusion equation for fluid density. Spatial and temporal variability of mixing in the Kitikmeot Sea is investigated. Furthermore, the contributions from a number of energy sources to the mixing process have been estimated in order to identify the main driving mechanism for mixing. Investigation of the temporal variability in diffusivity reveals seasonal patterns which can be attributed to the annual cycle of sea-ice coverage. It was found that wind stirring and convection due to sea-ice forming and sea-surface cooling make significant energy contributions to mixing in the Kitikmeot Sea.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".