Snow on sea ice in the Arctic Archipelago
Bibliographic record
Abstract
Snow depth on sea ice is an important control of sea ice growth and uncertainties in snow depth are one of the largest sources of uncertainties in estimating sea ice thickness with satellite based remote sensing methods. Currently the standard snow depth estimation product is a modified Warren 1999 snow depth climatology. The warren 1999 climatology was constructed from Soviet in situ snow depth measurements in the Arctic with no measurements procured in the Canadian Arctic Archipelago; resulting in a need for improved estimates of snow depth in the Canadian Arctic Archipelago. The Canadian Arctic Archipelago is an area of great importance being projected to be part of the last area to have year round ice and is underrepresented in panArctic data products. \n \nA number of snow depth climatologies were created for this thesis from in situ snow depth measurements taken on landfast sea ice along the coasts of Canada for the months of October through April. These Climatologies showed promising results for the Canadian Arctic Archipelago and the northeastern coast of mainland Canada, but does not show as promising results beyond the edges of the Canadian Arctic Archipelago into the Arctic Ocean, \n \nWhen compared to the modified Warren 1999 snow depth climatology and the SnowModel LG, the interpolation methods created in this thesis produced lower snow depth estimates in the autumn and early winter and greater snow depth later in the winter and in the spring for the Canadian Arctic Archipelago. The snow depth accumulation rates were found by these interpolation methods to be more evenly distributed through the months studied than what was found by the w99m climatology and the SnowMode-LG
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".