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Record W2150369596 · doi:10.1139/x07-125

Effects of time since stand-replacing fire and overstory composition on live-tree structural diversity in the boreal forest of central Canada

2008· article· en· W2150369596 on OpenAlexaffvenueabout
Brian W. Brassard, Han Y. H. Chen, Jian R. Wang, Peter N. Duinker

Bibliographic record

VenueCanadian Journal of Forest Research · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsLakehead UniversityDalhousie University
FundersMinistry of Environment
KeywordsDiameter at breast heightBorealForestryTaigaStand developmentGeographyEnvironmental scienceEcologyBiology

Abstract

fetched live from OpenAlex

Stand structure diversity is hypothesized (i) to increase with stand development and (ii) to be greater in mixedwood stands than in conifer and broadleaf stands. We examined the effects of time since stand-replacing fire (TSF) and overstory type on stand volume, stand density, and tree-size variability, which is measured using Shannon’s diversity index (H′) and coefficient of variation, in fire-origin boreal forest stands. We sampled 36 stands representing conifer, mixedwood, and broadleaf overstory types, ranging in ages from 72 to 201 years TSF on upland mesic sites in northwestern Ontario, Canada. Stand volume decreased in older mixedwood and broadleaf stands, but followed a U-shaped pattern in conifer stands with TSF. Diameter-at-breast-height-based H′ followed an inverse U-shaped pattern with TSF for all overstory types. Height-based H′ decreased with TSF in conifer and mixedwood stands but peaked at the intermediate age class in broadleaf stands. Diameter-at-breast-height- and height-based coefficient of variation indices followed an inverse U-shaped distribution with TSF. Our results partially supported the two hypotheses, as (i) the 124- to 139-year-old stands were most diverse and (ii) mixedwood stands were more than or as equally diverse as conifer and broadleaf stands, depending on stand development stage and the diversity indices used.

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)0.001
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 score0.057
Threshold uncertainty score0.382

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.218
Teacher spread0.206 · 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
Published2008
Admission routes3
Has abstractyes

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