Meteorological, snow and soil data (2013–2019) from a herb tundra permafrost site at Bylot Island, Canadian high Arctic, for driving and testing snow and land surface models
Bibliographic record
Abstract
Abstract. Seasonal snow covers Arctic lands 6 to 10 months of the year and is therefore an essential element of the Arctic geosphere and biosphere. Yet, even the most sophisticated snow physics models are not able to simulate fundamental physical properties of Arctic snowpacks such as density, thermal conductivity and specific surface area. The development of improved snow models is in progress, but testing requires detailed driving and validation data for high Arctic herb tundra sites, which are presently not available. We present 6 years of such data for an ice-wedge polygonal site in the Canadian high Arctic, in Qarlikturvik valley on Bylot Island at 73.15∘ N. The site is on herb tundra with no erect vegetation and thick permafrost. Detailed soil properties are provided. Driving data are comprised of air temperature, air relative and specific humidity, wind speed, shortwave and longwave downwelling radiation, atmospheric pressure, and precipitation. Validation data include time series of snow depth, shortwave and longwave upwelling radiation, surface temperature, snow temperature profiles, soil temperature and water content profiles at five depths, snow thermal conductivity at three heights, and soil thermal conductivity at 10 cm depth. Field campaigns in mid-May for 5 of the 6 years of interest provided spatially averaged snow depths and vertical profiles of snow density and specific surface area in the polygon of interest and at other spots in the valley. Data are available at https://doi.org/10.5885/45693CE-02685A5200DD4C38 (Domine et al., 2021). Data files will be updated as more years of data become available.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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 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".