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Record W2024751930 · doi:10.1029/2005jd006815

Temporal variability in area burned for the province of Ontario, Canada, during the past 200 years inferred from tree rings

2006· article· en· W2024751930 on OpenAlexafffundabout
Martin P. Girardin, Jacques Tardif, Mike Flannigan

Bibliographic record

VenueJournal of Geophysical Research Atmospheres · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of WinnipegNatural Resources CanadaCanadian Forest Service
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBorealDendrochronologyContext (archaeology)Physical geographyGeographyPeriod (music)Variance (accounting)Term (time)TaigaClimatologyGeologyEnvironmental scienceArchaeologyForestry

Abstract

fetched live from OpenAlex

Area burned variability in the province of Ontario, Canada, was inferred from 25 tree ring width chronologies covering A.D. 1781–1982 and distributed largely across the Boreal Shield. The area burned estimates account for 39.5% of the variance in the actual area burned recorded from 1917 to 1981 and were verified using a split sample calibration‐verification scheme. The reconstruction showed that a positive trend in area burned from circa 1970–1981 was preceded by three decades during which area burned was amongst the lowest during the past 200 years. The area burned exhibited a trend toward increasing variance during the past century, recently reaching magnitudes similar to those seen prior to 1850. Signal analyses further identified the presence of two prominent periodic components in area burned that related to decade‐to‐decade variations. This will help to place the recent increase in area burned in a context relative to the long‐term history of the province.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.015
Threshold uncertainty score0.074

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.011
GPT teacher head0.240
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), 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

Citations23
Published2006
Admission routes3
Has abstractyes

Explore more

Same venueJournal of Geophysical Research AtmospheresSame topicFire effects on ecosystemsFrench-language works237,207