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Record W1980728912 · doi:10.3189/002214311796405924

Thin-blade penetration resistance and snow strength

2011· article· en· W1980728912 on OpenAlexafffund
Chris Borstad, D. M. McClung

Bibliographic record

VenueJournal of Glaciology · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsPenetration (warfare)Materials scienceSlabUltimate tensile strengthPenetration testCompactionSnowComposite materialGeotechnical engineeringPerpendicularGeologyStructural engineeringGeometryEngineeringMathematics

Abstract

fetched live from OpenAlex

Abstract A thin-blade snow hardness gauge was developed that measures penetration resistance over a length scale (on the order of 10–100 grain contacts) relevant to the fracture of slab avalanches. A thin blade was chosen to measure the ruptures of bonds and grain structures and minimize the effects of snow compaction during penetration. The apparatus consists of a 10 cm wide, 0.6 mm thick stainless-steel blade attached to a digital push–pull gauge. Blade penetration measurements are easy to conduct in the field and laboratory and required no post-processing or subjective interpretation. Measurements were conducted in snow pits to test the effects of penetration rate, blade orientation and blade width. The blade hardness index, defined as the maximum force of penetration, is a highly repeatable measure across observers compared to the hand hardness test. The blade hardness index was a better variable than the density for correlating with tensile strength measurements in a cold laboratory and with a cohesive strength measure in the field. As strength is one of the most important parameters in the fracture mechanics of slab avalanches, the strong correlation between thin-blade penetration and strength should benefit future slope stability evaluations using this gauge.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.012
GPT teacher head0.211
Teacher spread0.198 · 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.

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

Citations21
Published2011
Admission routes2
Has abstractyes

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