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Record W48241718

Measuring Near-Surface Snow Temperature Changes Over Terrain

2006· article· en· W48241718 on OpenAlexaff
Laura Bakermans, Bruce Jamieson

Bibliographic record

VenueProceedings of the 2006 International Snow Science Workshop, Telluride, Colorado · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsSnowpackSnowDaytimeEnvironmental scienceAtmospheric sciencesTerrainCloud coverTemperature measurementSnow coverClimatologyClimate changeEnergy balanceDiurnal temperature variationMeteorologyGeologyGeography
DOInot available

Abstract

fetched live from OpenAlex

The energy balance at the snow surface results in substantial diurnal temperature fluctuations within the top portion of the snowpack. These temperature fluctuations, which vary both spatially and over time, can have important effects on stability. During the winters of 2005 and 2006, field data for a near-surface warming study were collected on a treeline knoll located in the Columbia Mountains of British Columbia. Thermocouple arrays placed on the top and at different locations on the knoll side slopes recorded temperatures within the top 30 cm of the snowpack. During each test period, nearby measurements of incoming short and long wave radiation were collected, as were periodic observations of cloud cover, air temperature and the snow surface temperature at each array. The study aims to use field data to identify when, and to what extent, near-surface warming occurs on different aspects. Instrumentation challenges encountered in accurately measuring near-surface snow temperatures are also presented. Preliminary analysis of the data shows a significant correlation between aspect and the maximum daytime increase in near-surface snow temperatures. In one experiment, the magnitude of daytime warming measured at 10 cm depth was almost 7 oC greater on the south aspect than the north aspect. The aspect dependent differences in daily temperature change decreased with increased cloud cover.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.030
Threshold uncertainty score0.711

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.000
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 head0.016
GPT teacher head0.218
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 teacher head, 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

Citations11
Published2006
Admission routes1
Has abstractyes

Explore more

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