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Record W4407343836 · doi:10.3389/fevo.2025.1504983

Coastal moderation of Holocene fire and vegetation change on the Pacific coast of Canada

2025· article· en· W4407343836 on OpenAlexafffundabout
M. Duncan, Marlow G. Pellatt, Karen E. Kohfeld

Bibliographic record

VenueFrontiers in Ecology and Evolution · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsParks CanadaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaMitacsParks Canada
KeywordsHoloceneVegetation (pathology)OceanographyModerationEast coastFire regimeClimate changeGeographyEnvironmental scienceGeologyPhysical geographyEcologyEcosystem

Abstract

fetched live from OpenAlex

In the coming century, climate variability is projected to increase in Northeast Pacific coastal areas, increasing the need for land managers to understand how ecosystems are expected to change in response to new or enhanced disturbances. Previous research indicates that the Pacific coast of Canada, southern British Columbia (BC) experienced warmer and drier climate conditions than present, with higher than modern fire activity during the early Holocene xerothermic interval (9.5 – 7.0 kcal BP). In this study, we reconstructed past climate-fire-vegetation changes from a 13,000-year sediment record from Lost Lake in Vancouver’s Lower Seymour Conservation Reserve, BC. Contrary to other sites, the coastal western hemlock forest at this site remained cool and moist with low fire activity throughout the xerothermic period. Instead, peak fire frequencies were observed during the cool and moist Neoglacial period (4.5 kcal BP – present), when human activity became prevalent. These results have implications for the managed watershed’s resilience to fire and response to future warming conditions.

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 score0.015
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.197
Teacher spread0.187 · 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

Citations0
Published2025
Admission routes3
Has abstractyes

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