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Magnitude and Frequency of Avalanches in Relation to Terrain and Forest Cover

2003· article· en· W2174988791 on OpenAlexafffund
D. M. McClung

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2003
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCryospheric studies and observations
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsMagnitude (astronomy)TerrainSnowFrequency analysisEnvironmental scienceGeologyGeodesyMeteorologyGeographyStatisticsMathematicsPhysicsCartography

Abstract

fetched live from OpenAlex

This paper contains an analysis of magnitude and frequency of avalanches in relation to terrain and forest cover variables. The analysis was applied to 194 avalanche paths in four avalanche areas along highways in British Columbia with approximately 25,000 avalanches recorded. The magnitude and frequency for the avalanche paths were estimated from data collected along the highways by avalanche technicians. Results show that mean magnitude and mean frequency are weakly correlated for a set of avalanche paths in an avalanche area. In addition, with magnitude and frequency viewed as response variables, magnitude and frequency correlate with different sets of predictor variables from one area to another. This paper contains the first comparison of variables which correlate with magnitude and frequency from one avalanche area to another. The results show that previous studies conducted for single areas are simplistic. However, there is some consistency between areas. Avalanche frequency is most directly related to terrain steepness and snow supply. Average avalanche magnitude appears related to terrain steepness, starting zone, and track confinement and the scale (e.g., total vertical drop of the path) with only indirect evidence for a link to snow supply.

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 score0.006
Threshold uncertainty score0.993

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.044
GPT teacher head0.291
Teacher spread0.247 · 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

Citations37
Published2003
Admission routes2
Has abstractyes

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