Snow Depth on Sea Ice and on Land in the Canadian Arctic from Long-Term Observations
Bibliographic record
Abstract
Intra-annual and decadal observations of snow depth on sea ice and on terrestrial land are examined within the Canadian Arctic. In situ snow depth measurements at 11 study sites spanning 1955–2019 form the basis of the analysis. Ice chart data acquired via the Canadian Ice Service are used to establish sea ice break-up and freeze-up dates and assess their impact on snow depth evolution. We find that on-ice and on-land snow accumulation in autumn differ due to the lag between the freeze-up and the first snow of the season. Once sea ice consolidates, on-ice and on-land snow depth become positively correlated in winter (p < 0.05). The mean seasonal rate of snow accumulation on sea ice from September to April is 3.2 ± 0.6 cm month−1 across the Canadian Arctic. Snow depth on terrestrial land is generally higher than on sea ice in the southern Canadian Arctic by up to 20–30 cm; but snow depth on sea ice tends to exceed that on land in the northern Canadian Arctic from winter to spring. Four sites (Eureka, Resolute, Cambridge Bay and Hall Beach) with continuous long-term records are selected for interannual analysis. Decadal trends in on-ice snow depth are mostly negative from autumn to spring. Autumn and spring snowfall have increased at three of the four sites. The Canadian Arctic experiences warming on a decadal scale, especially in autumn, by 0.5 to 0.8°C decade−1. Sea ice freeze-up is delayed by up to 2.5 days decade−1 in the southern Canadian Arctic, whereas break-up occurs earlier by about 3 days decade−1 in the northern Canadian Arctic.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".