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Record W3128354555 · doi:10.25316/ir-14983

Peatland vegetation response post-fire in a changing climate

2020· article· en· W3128354555 on OpenAlexaboutno aff
Chris Newton

Bibliographic record

VenueVIURRSpace (Vancouver Island University) · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsVegetation (pathology)PeatEnvironmental scienceClimate changePhysical geographyForestryHydrology (agriculture)GeographyGeology

Abstract

fetched live from OpenAlex

Peatland communities in western Canada have slowly developed over thousands of years with wildfires being a constant influence on these systems. As fires move through mature peatland communities, the aftermath is an open landscape where pioneer peatland species establish and develop. The open landscape supports the growth of successional species to create a mature forest, which is then ready for the fire interval cycle to continue. Fire cycles have been a constant on the landscape with little disruption; however, as climate change in western Canada has altered precipitation and temperature regimes, typical vegetation succession patterns that establish after peatland fires may be changing. The Chisholm fire of 2001 burned over 116,000 hectares of forest in northern Alberta, with most of the area being peatlands (treed fens). Vegetation surveys were completed throughout 2018 and 2019 within the burned peatlands of the Chisholm area and compared to an unburnt control area to identify species richness, diversity, composition and vegetation trends. I found, within the re-establishing peatland, a healthy, thriving and diverse community that is developing towards a community similar to the offsite mature treed fen. After almost 20 years of recovery, the affected vegetation community is dominated by peatland species. With temperatures and precipitation levels continually changing, the area is at a transition state in which the community may be maintained on the landscape or the area may experience a regime shift to a drier state.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.502
Threshold uncertainty score0.579

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.190
Teacher spread0.183 · 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 designNot applicable
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

Citations0
Published2020
Admission routes1
Has abstractyes

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