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Record W2101120726 · doi:10.14430/arctic710

Plant Diversity and Cover after Wildfire on Anthropogenically Disturbed and Undisturbed Sites in Subarctic Upland <i>Picea mariana</i> Forest

2002· article· en· W2101120726 on OpenAlexvenueaboutno aff
Stephanie Nowak, G. Peter Kershaw, Linda J. Kershaw

Bibliographic record

VenueARCTIC · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsRevegetationEnvironmental scienceSubarctic climateDisturbance (geology)Vegetation (pathology)EcologyIntermediate Disturbance HypothesisDiversity indexSpecies diversityHydrology (agriculture)GeologyEcological successionSpecies richnessBiologyGeomorphology

Abstract

fetched live from OpenAlex

Postfire development of cover and diversity was studied in an upland Picea mariana-dominated forest in the Canadian Subarctic. Short-term vegetation responses of 10- and 22-year-old cleared rights-of-way and a forest site were investigated two and three growing seasons after a wildfire. Prefire and postfire investigation of the study site allowed direct comparison of species cover and frequency values, as well as the Shannon-Wiener diversity index, before and after the fire. The fire considerably reduced diversity on all sites. Species diversity increased with the level of prefire disturbance. Prefire disturbance influenced the fire's characteristics by altering the fuel load and soil moisture, which in turn affected the postfire revegetation through different soil and microclimatic conditions. The sites that were most severely disturbed before the fire experienced the most rapid revegetation, including the highest diversity index and highest plant cover. Of the sites that were undisturbed before the fire, the natural drainage swales offered the best growing conditions after the burn. Furthermore, prefire disturbance increased the patchiness of the burned area, and the residual flora of unburned patches added to postfire floristic diversity.

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 categoriesMeta-epidemiology (narrow)
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 score1.000

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 science0.0000.001
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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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.

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

Citations11
Published2002
Admission routes2
Has abstractyes

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Same venueARCTICSame topicFire effects on ecosystemsFrench-language works237,207