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Record W2166274840 · doi:10.1139/x01-141

Evaluating a century of fire patterns in two Rocky Mountain wilderness areas using digital fire atlases

2001· article· en· W2166274840 on OpenAlexvenueno aff
Matthew G. Rollins, Thomas W. Swetnam, Penelope Morgan

Bibliographic record

VenueCanadian Journal of Forest Research · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsWildernessWilderness areaFire regimeVegetation (pathology)GeographyElevation (ballistics)Fire protectionPeriod (music)Fire ecologyForestryPhysical geographyFire historyUnderstoryEnvironmental scienceArchaeologyEcologyClimate changeEcosystemCanopyEngineering

Abstract

fetched live from OpenAlex

Changes in fire size, shape, and frequency under different fire-management strategies were evaluated using time series of fire perimeter data (fire atlases) and mapped potential vegetation types (PVTs) in the Gila – Aldo Leopold Wilderness Complex (GALWC) in New Mexico and the Selway–Bitterroot Wilderness Complex (SBWC) in Idaho and Montana. Relative to pre-Euro-American estimates, fire rotations in the GALWC were short during the recent wildfire-use period (1975–1993) and long during the pre-modern suppression period (1909–1946). In contrast, fire rotations in the SBWC were short during the pre-modern suppression period (1880–1934) and long during the modern suppression period (1935–1975). In general, fire-rotation periods were shorter in mid-elevation, shade-intolerant PVTs. Fire intervals in the GALWC and SBWC are currently longer than fire intervals prior to Euro-American settlement. Proactive fire and fuels management are needed to restore fire regimes in each wilderness complex to within natural ranges of variability and to reduce the risk of catastrophic wildfire in upper elevations of the GALWC and nearly the entire SBWC. Analyses of fire atlases provide baseline information for evaluating landscape patterns across broad landscapes.

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 score0.247
Threshold uncertainty score0.769

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.359
Teacher spread0.300 · 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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