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Record W1943944021 · doi:10.1139/cjfr-2015-0079

Vulnerability of timber supply to projected changes in fire regime in Canada’s managed forests

2015· article· en· W1943944021 on OpenAlexafffundvenueabout
Sylvie Gauthier, Pierre Y. Bernier, Yan Boulanger, Jian Guo, Luc Guindon, André Beaudoin, Dominique Boucher

Bibliographic record

VenueCanadian Journal of Forest Research · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsNatural Resources CanadaCanadian Forest Service
FundersCanadian Forest ServiceU.S. Forest Service
KeywordsVulnerability (computing)TaigaFire regimeGeographyClimate changeBorealNatural resource economicsForestryEnvironmental scienceAgroforestryEnvironmental resource managementEcologyEconomicsEcosystem

Abstract

fetched live from OpenAlex

The frequency of forest fires is predicted to increase in Canada, which may affect the availability of timber for industrial purposes. We therefore carried out an evaluation of the timber supply vulnerability to current and future fire risk through simplified calculations involving historical forest growth and harvest rates and current and projected forest burn rates. Calculations were performed at the level of forest management areas (FMAs) across the boreal and montane ecozones of Canada. For some FMAs, the vulnerability of timber supply to fire was estimated to be high to extreme by the middle of the century. For those FMAs, the increases in tree growth necessary to negate these risks were generally unrealistic. A modest simulated decrease in tree growth over time, however, was sufficient to raise the vulnerability of many other FMAs from low to moderate. Known biases in the analysis suggest that our assessment might underestimate the level of vulnerability in all FMAs. Other natural disturbances are not included in the analysis but their impact on timber supply may be additive to that of fire. Some adaptation measures to face these increasing risks are discussed.

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)0.004
metaresearch head score (Gemma)0.002
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 score0.022
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.041
GPT teacher head0.292
Teacher spread0.251 · 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

Citations79
Published2015
Admission routes4
Has abstractyes

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