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Record W2103782670 · doi:10.1139/x05-205

Patch structure, fire-scar formation, and tree regeneration in a large mixed-severity fire in the South Dakota Black Hills, USA

2005· article· en· W2103782670 on OpenAlexvenueno aff
Leigh B. Lentile, Frederick W. Smith, Wayne D. Shepperd

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsCambiumRegeneration (biology)Fire regimeForestryTaigaEcologyBotanyBiologyGeographyEcosystem

Abstract

fetched live from OpenAlex

We compared patch structure, fire-scar formation, and seedling regeneration in patches of low, moderate, and high burn severity following the large (~34 000 ha) Jasper fire of 2000 that occurred in ponderosa pine (Pinus ponderosa Dougl. ex P. & C. Laws.) forests of the Black Hills of South Dakota, USA. This fire created a patchy mosaic of effects, where 25% of the landscape burned as low, 48% as moderate, and 27% as high severity. Dead cambium on a significant portion of tree circumference in a tree with live cambium and a vigorous crown was taken as evidence of incipient fire-scar formation. Tree mortality was approximately 21%, 52%, and 100% in areas of low, moderate, and high burn severity, respectively. Dead cambium was detected on approximately 24% and 44% of surviving trees in low and moderate burn severity patches, respectively. Three years postfire, regeneration densities were ~612 and 450 seedlings·ha –1 in low and moderate burn severity patches, respectively, and no regeneration was observed in the interior of high burn severity patches. Fire-scars will be found on 73% of the area burned in this fire, and large patches of multicohort forest will be created. Mixed-severity fire may have been common historically in the Black Hills, and in conjunction with frequent surface fire, played an important role in shaping a spatially heterogeneous, multicohort ponderosa pine forest.

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)0.000
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 score0.926
Threshold uncertainty score0.963

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0000.000
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 head0.017
GPT teacher head0.253
Teacher spread0.236 · 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

Citations97
Published2005
Admission routes1
Has abstractyes

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