Pre-Melt Energy Budget of an Arctic Snowpack on Landfast First-Year Sea Ice
Bibliographic record
Abstract
The pre-melt energy budget of a snowpack on landfast first-year sea ice at a remote site in the Canadian Arctic Archipelago was analyzed. Over a 19-day period, the total heat conducted into the snowpack at the snow–sea-ice interface was the largest single energy transfer to the snowpack, while each of the turbulent heat fluxes removed comparable amounts of energy. The total energy transferred from the snowpack (∑Q ≈ −7027 kJ m−2) should have reduced its temperature; however, the opposite occurred. The snowpack’s temperature at both the 7 and 13 cm depths increased over the pre-melt period. The total change in internal energy and latent heat of the snowpack (ΔUsnowpack), derived from 15-minute changes in the snowpack’s temperature over the pre-melt period, was approximately 672 kJ m−2. Closure of the energy budget was not achieved for either the daily or the total pre-melt period. The terms of the energy budget were determined independently; thus, the failure to close the energy budget was the result of the accumulation of errors associated with all the terms. However, for snow on first-year sea ice, the parameterization of the salinity and temperature dependence of the “specific heat” of the basal layer of the snowpack was likely the primary source of error. The snowpack plays a central role in the transfer of energy across the ocean–sea-ice–atmosphere interface, but an adequate method for modelling the evolution of snow on Arctic sea ice including the energy budget, which determines the warming rate and subsequent melt rate of the snow, has yet to be developed.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".