Modeling arctic snow distribution and melt at the 1 km grid scale*
Bibliographic record
Abstract
Snow accumulation, re-distribution and melt are important hydrological considerations in the Arctic. This study presents a model of the late-winter snow cover and the ensuing snowmelt in a High Arctic environment at a scale of 1 km. Indexing is used to spread the snow data from a lowland weather station to various terrain units over a 16×13 km2 target area east of Resolute, Cornwallis Island, Canada. Meteorological variables measured at this base station are spatially extended by field derived empirical relationships for the computation of melt at various terrain units using the energy balance approach. These melt rates are weighted by the fractional coverage of various terrain unit within each 1×1 km2 cell. The snow distribution pattern is obtained daily and model performance was tested by comparing observed and computed dates of melt and the radiation balance over snow. The simulated snow pattern compared favourably with the snow cover imaged by LANDSAT. Daily changes in the probability distribution of snow water equivalent over the target area was examined and snow depletion curves were derived. They describe sub-grid variability over an area and our results point to several assumptions that should be scrutinized in sub-grid parameterization of snow distribution.
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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 | 0.000 | 0.000 |
| 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".