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Record W2054211024 · doi:10.3189/172756410791386616

Using stability tests and regional avalanche danger to estimate the local avalanche danger

2010· article· en· W2054211024 on OpenAlexafffundabout
Laura Bakermans, Bruce Jamieson, Jürg Schweizer, Pascal Haegeli

Bibliographic record

VenueAnnals of Glaciology · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsCanadian Avalanche AssociationSimon Fraser UniversityUniversity of Calgary
FundersParks Canada
KeywordsRecreational useStability (learning theory)RecreationPhysical geographyGeologyEnvironmental scienceGeographyComputer scienceLawPolitical science

Abstract

fetched live from OpenAlex

Abstract Because public avalanche forecast regions in Canada are large, ranging from 100 to >30 000 km2, there are often areas within each region where the current local avalanche danger differs from the forecast regional danger. Identifying areas where the local danger is higher or lower than the regional rating is useful for recreational backcountry travellers; for those with limited experience, however, this is not always practical. During four winters in the Columbia, Coast and Rocky Mountains of western Canada, field teams performed stability tests and undertook local avalanche danger assessments for comparison against the regional danger ratings. Significant correlations between stability test variables and the local avalanche danger, and between stability test variables and the difference between local and regional danger, indicate potential for improved evaluation of the local danger if stability test results are considered with the regional bulletin rating. Although our analysis shows that a single stability test result cannot reliably be used to estimate the local avalanche danger, it does identify circumstances under which stability tests can help backcountry recreationists identify an area of locally higher avalanche danger.

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)0.000
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 score0.202
Threshold uncertainty score0.708

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.056
GPT teacher head0.347
Teacher spread0.291 · 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

Citations16
Published2010
Admission routes3
Has abstractyes

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