Linear mixed modelling of snow distribution in the central Yukon
Bibliographic record
Abstract
Abstract Remote sensing estimates of snow water equivalent (SWE) in mountainous areas are subject to large uncertainties. As a prerequisite for testing passive microwave algorithm estimations of SWE, this study aims to collect snow depth (SD) data and provide an understanding of its complex spatial structure as part of the Canadian International Polar Year observations theme. Snow accumulation, redistribution and ablation are controlled by processes that depend on a variety of topographic factors as well as land surface characteristics, which leads us to modelling SD as a function of proxy variables derived from digital elevation model and Landsat data. Field measurements were performed at 3924 locations compromising 184 sites in 50 transects over 2 years. These measurements were used to predict SD over the study area using a spatial linear mixed‐effects model, a model type capable of handling the hierarchical structure of the field data. The model, built using stepwise variable selection, uses as predictor variables transformed elevation, slope, the logarithm of slope, potential incoming solar radiation and its transform; the normalized difference vegetation index, and a transformed tasseled cap brightness from Landsat imagery. A second, simpler model links SD with density giving SWE. The cross‐validated root mean squared error of the SD distribution model was 14 cm around an overall mean of 80 cm over a domain of 250 × 250 km. This instantaneous end‐of‐season peak‐accumulation snow map will enable the validation of satellite remote sensing over a generally inaccessible area. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".