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Record W2073717859 · doi:10.1179/env.2002.7.1.1

Ancient Fires on Southern Vancouver Island, British Columbia, Canada: A Change in Causal Mechanisms at about 2,000 ybp

2002· article· en· W2073717859 on OpenAlexaffabout
Kendrick J. Brown, Richard J. Hebda

Bibliographic record

VenueEnvironmental Archaeology · 2002
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsRoyal British Columbia Museum
Fundersnot available
KeywordsHoloceneCharcoalClimate changeBefore PresentPhysical geographyHolocene climatic optimumRange (aeronautics)Period (music)GeographyGeologyClimatologyArchaeologyOceanography

Abstract

fetched live from OpenAlex

Charcoal records were examined from seven sediment cores and two stratigraphic sections on southern Vancouver Island, Canada. Charcoal influx and climate trend regressions were established using high order polynomial functions. During the late-glacial (ca. 13,000–10,000 ybp), variations in the charcoal record suggest that fires likely responded to changes in fuel availability and climate. The high incidence of early-Holocene (ca. 10,000–7,000ybp) fires may have been partly modified by human activity, though it seems more likely that climate exerted the greatest control. A decrease in fires during the mid- and early late-Holocene from 7,000–4,000 and 4,000–2,000 ybp respectively is consistent with a regional moistening trend, implying that fires were climatically limited. In the late late-Holocene from 2,000 ybp–present, several sites record an increase in charcoal influx even though climate was continuing to moisten and cool, suggesting that non-climatic factors were responsible for the observed increase in fire activity. Estimates of native populations range up to thousands of people for southern Vancouver Island before the arrival of Europeans. These people were knowledgeable of fire, suggesting that humans were responsible for the increase in fires during the late late-Holocene cool, moist interval.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.127
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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0710.001

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 head0.011
GPT teacher head0.175
Teacher spread0.164 · 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; both teacher heads agree on what is shown here.

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

Citations51
Published2002
Admission routes2
Has abstractyes

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