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Record W2148884300 · doi:10.1016/j.yqres.2013.03.001

Holocene vegetation history and fire regimes of <i>Pseudotsuga menziesii</i> forests in the Gulf Islands National Park Reserve, southwestern British Columbia, Canada

2013· article· en· W2148884300 on OpenAlexfundaboutno aff
Jennifer D. Lucas, Terri Lacourse

Bibliographic record

VenueQuaternary Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWoodlandCharcoalVegetation (pathology)National parkHoloceneFire regimeUnderstoryForestryTephraPhysical geographyGeologyGeographyPaleoecologyEcologyArchaeologyEcosystemVolcanoCanopy

Abstract

fetched live from OpenAlex

Abstract Pollen analysis of a 9.03-m-long lake sediment core from Pender Island on the south coast of British Columbia was used to reconstruct the island's vegetation history over the last 10,000 years. The early Holocene was characterized by open mixed woodlands with abundant Pseudotsuga menziesii and a diverse understory including Salix and Rosaceae shrubs and Pteridium aquilinum ferns. The establishment of Quercus garryana savanna-woodland with P. menziesii and Acer macrophyllum followed deposition of the Mazama tephra until ~ 5500 cal yr BP, when these communities gave way to modern mixed P. menziesii forest. Charcoal analyses of the uppermost sediments revealed low charcoal accumulation over the last 1300 years with a mean fire return interval (mFRI) of 88 years. Fires were more frequent (mFRI = 50 yr) during the Medieval Climate Anomaly (MCA) with warm, dry conditions facilitating a higher fire frequency than during the Little Ice Age, when fires were infrequent. Given the projected warming for the next 50–100 years, land managers considering the reintroduction of fire to the Gulf Islands National Park Reserve may want to consider using the mFRI of the MCA as a baseline reference in prescribed burning strategies.

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 score0.091
Threshold uncertainty score0.407

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 head0.023
GPT teacher head0.248
Teacher spread0.225 · 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

Citations28
Published2013
Admission routes2
Has abstractyes

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