Measurements and controls on mid-winter alpine ground thermal regime in the Purcell mountains, British Columbia
Bibliographic record
Abstract
Alpine snow is an important water reservoir for mountain hydrology, climate, ecosystem functions, and has substantial economic value. Snowmelt during spring and summer is driven primarily by incoming shortwave and longwave radiation fluxes, and the ground heat flux is considered to be negligible during this time. However, during the accumulation phase, the ground heat flux may contribute to snowpack thermal conditions and midwinter melt, though this subject has not been studied extensively. The objective of this study is to quantify the alpine ground thermal regime and its relation to topographic setting and the overlying snowpack. The effects of elevation, maximum winter snow depth, snow cover duration, slope, ruggedness, aspect, total potential solar radiation, proximity to glacial ice, and depth of thermistor were evaluated. Four transects consisting of 29 temperature data loggers at the ground-snow interface and one meteorological station collected data from 16 August 2020 to 6 August 2021 at an alpine site in the Purcell Mountains in British Columbia. Snow cover duration, onset, and end-of-winter depths were found to have the greatest influence on the ground thermal regime. Total potential solar radiation had an inverted relation with ground temperatures, however, this was likely related to snow cover duration. Modeled ground heat flux scenarios revealed that snow depth or onset is the most influential of the variables tested. Snow thermal conductivity has the second greatest influence on total ground heat flux, however, true snow thermal conductivity likely varies throughout the winter season and was not measured in this study. Wind has the greatest influence on snow distribution within the Conrad basin with wind scoured slopes coincidentally sharing the same aspect as slopes that receive the greatest total potential solar radiation. There is little evidence to suggest that the ground thermal regime has any influence over the overlying snowpack.
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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 | 0.002 |
| 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".