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Record W2011881077 · doi:10.1139/e05-087

Snow avalanche activity after fire and logging disturbances, northern Gaspé Peninsula, Quebec, Canada

2005· article· en· W2011881077 on OpenAlexafffundvenueabout
Daniel Germaın, Louise Filion, Bernard Hétu

Bibliographic record

VenueCanadian Journal of Earth Sciences · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsUniversité du Québec à RimouskiUniversité LavalCenter for Northern Studies
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSnowGeologyLoggingPhysical geographyPeninsulaVegetation (pathology)Disturbance (geology)Snow coverHydrology (agriculture)GeomorphologyForestryGeographyArchaeology

Abstract

fetched live from OpenAlex

In mountainous areas, ecological disturbances causing forest fragmentation may influence the pattern and regime of snow avalanches. In the northern Gaspé Peninsula (Quebec), at two sites located on treed slopes of a south–north oriented valley, tree removal by fire and logging operations was the precursor factor for avalanche activity. Years of high-magnitude snow avalanches were identified based on tree-ring techniques; these avalanches were different from those identified by Dubé et al. (2004) for three undisturbed scree-slope sites in the same area. The lack of synchronicity in avalanche occurrences between disturbed and undisturbed sites suggests a strong influence of local factors (e.g., disturbance, local topography, slope aspect, vegetation). The first avalanche years were recorded in 1941 and 1988, after a fire in 1938 and logging operations in 1986–1987, respectively. Both of these years had above average snowfalls; this indicates that climate (total annual snowfall) was also a contributing factor for avalanche activity. The avalanche window in the post-logging site was shorter (four years) than that of the post-fire site (15–20 years). This is an indication that avalanche activity after tree removal largely depends on the capacity of woody vegetation to reach heights sufficient to control snow drifting and thus avalanche activity.

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 score0.105
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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations68
Published2005
Admission routes4
Has abstractyes

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