Insights of the seasonal evolution of an arctic snowpack from an intensive field campaign
Bibliographic record
Abstract
The Arctic snowpack covers a large portion of the Earth’s surface, yet detailed snow observations in these areas are sparse compared to observations in alpine environments. The Arctic presents unique environmental conditions, leading to thin snowpacks undergoing high-temperature gradients. These conditions lead to specific evolutions of the snow microstructure, which results in peculiar snowpack properties. To improve our understanding and description of the Arctic snowpack, an eight-month-long field campaign (IVORI) was conducted in Cambridge Bay at the Canadian High Arctic Research Station, Nunavut, Canada (69°N) during the 2023-2024 winter. The campaign is based on daily acquisitions of the 3D snow microstructure at 10 μm using a cold laboratory X-ray tomograph located next to the field site, along with extensive monitoring of the meteorological conditions and traditional snow characterizations. This dataset notably contains 200 tomographic samples and 50 snow stratigraphic profiles covering the full snow depth.Here we present the specific climatic context of the 2023-2024 winter at Cambridge Bay, along with an analysis of the evolution of the vertical profiles of density and specific surface area. Finally, a preliminary overview of the performance of snow models at this Arctic location is given, highlighting potential areas for improvement.
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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 | 0.001 |
| 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) | 0.001 | 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".