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Record W2122255213 · doi:10.1071/wf07024

Climate drivers of regionally synchronous fires in the inland Northwest (1651–1900)

2008· article· en· W2122255213 on OpenAlexaff
Emily K. Heyerdahl, Donald McKenzie, Lori D. Daniels, Amy Hessl, Jeremy S. Littell, Nathan J. Mantua

Bibliographic record

VenueInternational Journal of Wildland Fire · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of British Columbia
FundersUniversity of Washington
KeywordsEnvironmental scienceFire regimeEl Niño Southern OscillationBorealClimatologyPacific decadal oscillationSnowmeltSpring (device)Climate changeDrynessSnowPhysical geographyGeographyOceanographyEcosystemGeologyEcologyMeteorologyArchaeology

Abstract

fetched live from OpenAlex

We inferred climate drivers of regionally synchronous surface fires from 1651 to 1900 at 15 sites with existing annually accurate fire-scar chronologies from forests dominated by ponderosa pine or Douglas-fir in the inland Northwest (interior Oregon, Washington and southern British Columbia). Years with widespread fires (35 years with fire at 7 to 11 sites) had warm spring–summers and warm-dry summers, whereas years with no fires at any site (18 years) had the opposite conditions. Spring climate likely affected the length of the fire season via the effects of snowmelt on soil and fuel moisture, whereas summer climate influenced fuel moisture during the fire season. Climate in prior years was not a significant driver of regionally synchronous surface fires, likely because fuels were generally sufficient for the ignition and spread of such fires in these forests. Fires occurred significantly more often than expected by chance when the El Niño–Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) were both warm phase and less often when they were both cool phase. Interactions between large-scale climate patterns influenced fire synchrony in the inland Northwest because phases of ENSO and PDO were associated with changes in the frequency of warm-dry v. cool-wet spring–summer climate.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.283

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 science0.0010.000
Research integrity0.0000.000
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 head0.007
GPT teacher head0.214
Teacher spread0.207 · 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

Citations146
Published2008
Admission routes1
Has abstractyes

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