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Record W2049178379 · doi:10.5558/tfc81125-1

Influence of stand size on pattern of live trees in mixedwood landscapes following wildfire

2005· article· en· W2049178379 on OpenAlexafffundvenueabout
C. Smyth, Jim Schieck, Stan Boutin, Shawn Wasel

Bibliographic record

VenueThe Forestry Chronicle · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsAlberta Pacific Forest IndustriesUniversity of AlbertaAlberta Environment and Protected Areas
FundersUniversity of AlbertaAlberta-Pacific Forest Industries
KeywordsBorealTaigaForestryGeographyEnvironmental scienceEcologyAgroforestryBiology

Abstract

fetched live from OpenAlex

Burned areas and patches of residual live trees were mapped from post-fire aerial photographs of 168 mixedwood stands (a total of 9367 ha), in eight large wildfires in the boreal forest of northern Alberta. The stands were stratified into three size classes: small (<10 ha), medium (10–60 ha), and large (>60 ha).We described the area occupied by single live residual trees, unburned patches of live trees and partially burned patches of live trees within these mixedwood stands. Although results from individual stands were highly variable, there was proportionally more area covered by live residual trees in large fire-killed stands compared to medium stands, which in turn had proportionally more than small stands. For most sizes and types of live tree patches, larger fire-killed stands had a greater proportion of live tree area compared to smaller stands. Results of this study are compared to similar studies and current harvest guidelines. We outline the amount and distribution of live tree patches that would be needed to create harvest areas similar to that found after wildfire. Key words: natural disturbance, wildfire, live residual trees, ecosystem management, forest harvest pattern, boreal mixedwood

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 score0.091
Threshold uncertainty score0.338

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 head0.004
GPT teacher head0.208
Teacher spread0.204 · 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

Citations13
Published2005
Admission routes4
Has abstractyes

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