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FIRE DISTURBANCE PATTERNS AND FOREST AGE STRUCTURE

2001· article· en· W2027059031 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueNatural Resource Modeling · 2001
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsCanadian Forest Service
Fundersnot available
KeywordsDisturbance (geology)Distribution (mathematics)Age structureExponential distributionStability (learning theory)EcologyStable distributionGeographyPhysical geographyEnvironmental scienceDemographyBiologyMathematicsStatisticsPopulationComputer sciencePaleontology

Abstract

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ABSTRACT. This paper investigates how the stability of forest age‐distribution is related to the fire regimes. We start with the derivation of theoreticalnegative exponentialforest age‐distribution, and use three models to explore the condition with which a stable age‐distribution could be expected. The results suggested that a stable age‐distribution could always be achieved as long as the forest age‐specific mortality is constant over time, and the shape of a stable age‐distribution is mainly determined by the forest age‐specific mortality. However, the stability of forest age‐distribution will be reduced when a small variation in the age‐specific mortality is introduced. The simulation results of the possible patterns of the age‐distribution under various fire regimes indicated that a variety of age‐distribution curves could appear, including negative exponential and one or multiple peaks in the curves. The results suggested that a stable forest age‐distribution might never be achieved if the forest landscape is subjected to large and irregular fire disturbances.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.735
Threshold uncertainty score0.613

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 science0.0000.000
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 head0.205
Teacher spread0.198 · 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