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Record W2158876525 · doi:10.1093/forestry/cps061

The effects of site characteristics on the landscape-level windthrow regime in the North Shore region of Quebec, Canada

2012· article· en· W2158876525 on OpenAlexaffabout
Kaysandra Waldron, Jean‐Claude Ruel, Sylvie Gauthier

Bibliographic record

VenueForestry An International Journal of Forest Research · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaCanadian Forest ServiceUniversité Laval
Fundersnot available
KeywordsWindthrowShoreHydrology (agriculture)WildlifeGeologyEnvironmental scienceGeographyForestryEcologyOceanography

Abstract

fetched live from OpenAlex

Understanding windthrow is essential for the implementation of ecosystem management, especially in forests with long fire return intervals. Our study describes windthrow dynamics at a landscape scale of the Quebec North Shore region, Canada, and evaluates the effect of some site (soil surface material thickness or deposit thickness, drainage, slope, topography and wind), and stand (dominant species, height and density) characteristics on windthrow probabilities. The SIFORT database, created by the Ministry of Natural Resources and Wildlife of Quebec, the Quebec forest fire control agency and the Quebec forest pest and disease control agency, was used to perform a spatiotemporal analysis of windthrow, according to site and stand characteristics. Windthrow probabilities were influenced by topographic exposure (topex), slope classes and deposit thickness. Windthrow probabilities increased with topographic exposure. Windthrow occurrence was highest when deposit was thick (more than 1 m) and slope class was medium (from 15 to 30 per cent). Finally, this study has shown the importance of partial windthrow at the landscape scale in the North Shore region. Thus our results suggest that from an ecosystem management perspective, clear-cutting must be partly replaced by partial cuts, in order to emulate the regional dynamics of partial windthrow.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.539
Threshold uncertainty score0.889

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 science0.0020.000
Research integrity0.0000.001
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 head0.027
GPT teacher head0.284
Teacher spread0.257 · 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

Citations43
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueForestry An International Journal of Forest ResearchSame topicFire effects on ecosystemsFrench-language works237,207