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Record W2945588310 · doi:10.1594/pangaea.899744

Meteorological observations, snow volume change and insulation experiment data at Craftsbury Outdoors Center, Vermont in 2018

2019· dataset· en· W2945588310 on OpenAlexaboutno aff
Hannah Weiss, Paul R. Bierman

Bibliographic record

VenueFigshare · 2019
Typedataset
Languageen
FieldEnvironmental Science
TopicSmart Materials for Construction
Canadian institutionsnot available
Fundersnot available
KeywordsSnowVolume (thermodynamics)Center (category theory)Environmental scienceMeteorologyAtmospheric sciencesPhysical geographyHydrology (agriculture)ClimatologyGeographyGeologyGeotechnical engineeringPhysicsChemistryThermodynamics

Abstract

fetched live from OpenAlex

Climate change, including warmer winter temperatures, a shortened snowfall season, and more rain-on-snow events, threatens nordic skiing as a sport. In response, over-summer snow storage, attempted primarily using wood chips as a covering material, has been successfully employed as a climate change adaptation strategy by high-elevation and/or high-latitude ski centers in Europe and Canada. Such storage has never been attempted at a site with both a low altitude and latitude, and few studies have quantified snowmelt repeatedly through the summer. Such data, along with tests of different cover strategies, are prerequisites to optimizing snow storage strategies. Here, we assess the melt rates of two wood-chip covered snow piles (each ~200 m3) emplaced during spring 2018 in Craftsbury, Vermont (45o N and 360 m asl) to develop an optimized snow storage strategy. In 2019, we tested that strategy on a much larger, 9300 m3 pile. In 2018, we continually logged air-to-snow temperature gradients under different cover layers including rigid foam, open cell foam, and wood chips both with and without an underlying insulating blanket and an overlying reflective cover. We also measured ground temperatures to a meter depth both under and adjacent to the snow piles and used a snow tube to measure snow density. During both years, we monitored volume change over the melt season using terrestrial laser scanning. In 2018, snow volume loss ranged from -0.29 to -2.81 m3 day-1 with highest rates in mid-summer and lowest rates in the fall; mean melt rates were 1.24 and 1.50 m3 day-1, 0.6 to 0.7 % of initial pile volume per day. Snow density did increase over time but most volume loss was the result of melting. Wet wood chips underlain by an insulating blanket and covered with a reflective sheet was the most effective cover combination for minimizing melt, likely because the surface reflected incoming shortwave radiation while the wet wood chips provided significant thermal mass, allowing much of the energy absorbed during the day to be lost by long-wave emission at night. The importance of pile surface area to volume ratio is demonstrated by the melt rates of the 9300 m3 pile emplaced in 2019 which lost only <0.16% of its initial volume per day between April and September, retaining 75% of the initial snow volume over summer. Together, these data demonstrate the feasibility of over-summer snow storage at mid-latitudes and low altitudes and suggest efficient cover strategies.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: none
Teacher disagreement score0.649
Threshold uncertainty score0.706

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.179
GPT teacher head0.288
Teacher spread0.109 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2019
Admission routes1
Has abstractyes

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