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Record W2048597625 · doi:10.1016/j.yqres.2011.10.006

Impact of fire on long-term vegetation dynamics of ombrotrophic peatlands in northwestern Québec, Canada

2011· article· en· W2048597625 on OpenAlexafffundabout
Gabriel Magnan, Martin Lavoie, Serge Payette

Bibliographic record

VenueQuaternary Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of California, IrvineFonds Québécois de la Recherche sur la Nature et les TechnologiesUniversité Laval
KeywordsOmbrotrophicPeatBogEcological successionMacrofossilSphagnumVegetation (pathology)BorealMirePhysical geographyTaigaFire regimeGeologyEnvironmental scienceEcologyGeographyForestryEcosystemOceanographyHolocene

Abstract

fetched live from OpenAlex

A 7000-year record of local fire history was reconstructed from three ombrotrophic peatlands in the James Bay lowlands (northwestern Québec, Canada) using a high-resolution analysis of macroscopic charcoal (long axis≥0.5 mm). The impact of fire on vegetation changes was evaluated using detailed analysis of plant macrofossils. Compared to upland boreal forest, fire incidence in theseSphagnum-dominated bogs is rather low. Past fire occurrence seems to have been controlled primarily by internal processes associated with local hydroseral succession. Size of the peatland basin and distance from the well-drained forest soils also appear to be factors controlling fire occurrence. The impact of peatland fires on long-term vegetation succession appears negligible except in a forested bog, where it initiated the replacement ofSphagnumby mosses. In some circumstances, fire caused marked changes in the bryophyte assemblages over many decades. However, ombrotrophic peatland vegetation is generally resilient to surface fire.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.017
Threshold uncertainty score0.070

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.308
Teacher spread0.268 · 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 source (direct Gemma or distilled Codex), 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

Citations75
Published2011
Admission routes3
Has abstractyes

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