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Record W1963720980 · doi:10.1139/x04-177

Effects of fire severity on early development of understory vegetation

2005· article· en· W1963720980 on OpenAlexfundvenueaboutno aff
G. Geoff Wang, Kevin J Kemball

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsUnderstoryBorealVegetation (pathology)Species richnessPrescribed burnRegeneration (biology)Plant communityEcologyEnvironmental scienceFire regimeHerbaceous plantForestryBiologyEcosystemCanopyGeographyMedicine

Abstract

fetched live from OpenAlex

Four boreal mixedwood stands burned by the 1999 Black River wildfire in southeastern Manitoba were sampled to examine the effects of fire severity on early regeneration dynamics of understory vegetation. In each stand, three fire severity classes (scorched, lightly burned, and severely burned) were identified based on the degree of forest floor consumption, and six plots per severity class were randomly selected. Variation in fire severity significantly affected the initial regeneration of the understory plant community. Regeneration response after fire was largely controlled by interactions between fire severity and species' regeneration strategy. Establishment of invaders, seed bankers, and sprouters was best on severely burned, lightly burned, and scorched plots, respectively. Species richness and Shannon's diversity index was reduced by severe fire only in the first postfire year. However, the effects of fire severity on species abundance and composition persisted through the entire study period (1999–2002). Rapid changes in the understory plant community were only observed during the initial 3 postfire years, regardless of fire severity. At the end of the study, herbaceous plants were the most dominant component, with woody plants being a codominant component on scorched plots, and nonvascular plants being a codominant component on severely burned plots.

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 score0.194
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.021
GPT teacher head0.262
Teacher spread0.241 · 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

Citations108
Published2005
Admission routes3
Has abstractyes

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