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Record W4225322534 · doi:10.24124/2022/59284

Measurements and controls on mid-winter alpine ground thermal regime in the Purcell mountains, British Columbia

2022· dissertation· en· W4225322534 on OpenAlexaboutno aff
Kevin Ostapowich

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsnot available
Fundersnot available
KeywordsSnowpackSnowSnowmeltEnvironmental scienceShortwave radiationAtmospheric sciencesHeat fluxClimatologyHydrology (agriculture)GeologyMeteorologyGeographyHeat transferRadiation

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.136

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.023
GPT teacher head0.226
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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