Modeling arctic snow distribution and melt at the 1 km grid scale*
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.001 | 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 it