Implications of spatial distributions of snow mass and melt rate for snow-cover depletion: observations in a subarctic mountain catchment
Bibliographic record
Abstract
Abstract Spatial statistics of snow water equivalent (SWE) and melt rate were measured using spatially distributed, sequential ground surveys of depth and density in forested, shrub and alpine tundra environments over several seasons within a 185 km2 mountain catchment inYukonTerritory, Canada.When stratified by slope/aspect sub-units within landscape classes, SWE frequency distributions matched the log-normal, but multiclass surveys showed a more bimodal distribution. Within-class variability of winter SWE could be grouped into (i) windswept tundra and (ii) sheltered tund ra/forest regimes. During melt, there was little association between the standard deviation and mean of SWE. At small scales, a negative correlation developed between spatial distributions of pre-melt SWE and melt rate where shrubs were exposed above the snow. This was not evident in dense-forest, alpine-tundra or deep-snowdrift landscape classes. At medium scales, negative SWE and melt-rate correlations were also found between mean values from adjacent slope sub-units of the tundra landscape class. Themedium-scale correlation was likely due to slope effects on insolation and blowing-snow redistribution. At the catchment scale, the correlation between mean SWE and melt rate from various landscape classes reversed to a positive one, likely influenced by intercepted and blowing regimes, shrub exposure during melt and adiabatic cooling with elevation rise. Covariance at the catchment scale resulted in a 40% acceleration of snow depletion. These results suggest that the spatial variability and covariability of both SWE and melt rate are scale- and landscape-class-specific and need to be considered in a landscape-stratified manner at the appropriate scale when snow depletion is described and the snowmelt duration predicted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".