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Record W2099188498 · doi:10.1139/x01-093

Climatic and human influences on fire history in Pike National Forest, central Colorado

2001· article· en· W2099188498 on OpenAlexvenueno aff
Joseph A. Donnegan, Thomas T. Veblen, Jason S. Sibold

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersU.S. Geological SurveyU.S. Department of Agriculture
KeywordsDendrochronologyEnvironmental scienceFire regimeEl Niño Southern OscillationPacific decadal oscillationProxy (statistics)Period (music)ClimatologyGeographyPhysical geographyEcologyGeologyEcosystemArchaeologyBiology

Abstract

fetched live from OpenAlex

We investigated interannual and multidecadal variability in fire regimes, as related to climate and human land-use in Pike National Forest, central Colorado. Short and long-term trends in fire-scar records were related to tree-ring proxy records of moisture availability and to variability in El Niño – Southern Oscillation (ENSO). Fire occurrence is strongly tied to interannual drought conditions and is associated with cycles of ENSO. Fire events tend to occur in years of reduced moisture availability (La Niña years) and are often preceded by 2–4 years of increased moisture availability (El Niño years). A period of reduced variability in the tree-ring record from 1760 to 1820 AD, roughly corresponds to a period of reduced fire occurrence from approximately 1792 to 1842. Coincident with increased fire occurrence, variability in the climate proxies was high in the middle to late 1800s until the early 1900s. Multidecadal impacts through land use are also evident in the fire record with sharp increases during Euro-American settlement in ca. 1850 and abrupt declines with the start of active fire suppression after ca. 1920. Both humans and climatic variation appear to have interacted synergistically to create long-term trends in fire occurrence over the past two centuries.

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 score0.658
Threshold uncertainty score0.944

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 science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.295
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.

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

Citations88
Published2001
Admission routes1
Has abstractyes

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