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Record W2050771205 · doi:10.1139/x08-154

Future emissions from Canadian boreal forest fires

2009· article· en· W2050771205 on OpenAlexaffvenueabout
B. D. Amiro, Alan S. Cantin, Mike Flannigan, William J. de Groot

Bibliographic record

VenueCanadian Journal of Forest Research · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of ManitobaCanadian Forest Service
FundersForskningsrådet om Hälsa, Arbetsliv och Välfärd
KeywordsTaigaEnvironmental scienceBorealGreenhouse gasFuel efficiencyBiomass (ecology)Climate changeForestryAtmospheric sciencesGeographyEcologyEngineering

Abstract

fetched live from OpenAlex

New estimates of greenhouse gas emissions from Canadian forest fires were calculated based on a revised model for fuel consumption, using both the fire fuel load and the Drought Code of the Canadian Forest Fire Weather Index System. This model was applied to future climate scenarios of 2×CO 2 and 3×CO 2 environments using the Canadian Global Climate Model. Total forest floor fuel consumption for six boreal ecozones was estimated at 60, 80, and 117 Tg dry biomass for the 1×CO 2 , 2×CO 2 , and 3×CO 2 scenarios, respectively. These ecozones cover the boreal and taiga regions and account for about 86% of the total fire consumption for Canada. Almost all of the increase in fuel consumption for future climates is caused by an increase in the area burned. The effect of more severe fuel consumption density (kilograms of fuel consumed per square metre) is relatively small, ranging from 0% to 18%, depending on the ecozone. The emissions of greenhouse gases from all Canadian fires are estimated to increase from about 162 Tg·year –1 of CO 2 equivalent in the 1×CO 2 scenario to 313 Tg·year –1 of CO 2 equivalent in the 3×CO 2 scenario, including contributions from CO 2 , CH 4 , and N 2 O.

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)0.001
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 score0.049
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.272
Teacher spread0.256 · 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

Citations164
Published2009
Admission routes3
Has abstractyes

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