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Record W3155578318 · doi:10.5194/egusphere-egu21-4846

Spatial variability in Holocene wildfire responses to environmental change in the northern extratropics 

2021· article· en· W3155578318 on OpenAlexaff
David Kesner, Sandy P. Harrison, Tatiana Blyakharchuk, Mary E. Edwards, Michelle Garneau, Gabriel Magnan, Colin Prentice

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHoloceneExtratropical cycloneEnvironmental changeLatitudeClimate changeEnvironmental scienceEcologyPopulationEcosystemBiomass (ecology)Physical geographyGeographyBiologyDemographyArchaeologyMeteorology

Abstract

fetched live from OpenAlex

Fire is an important environmental and ecological process in northern high latitude environments. It is unclear how fire will respond to modern environmental change in this region and its implications for ecosystem processes and human societies. For insight into the long-term evolution of fire regimes, we reconstruct changes in biomass burning in the northern extratropics (>45°N) from the early Holocene (9000 years ago) to the present using the Reading Palaeofire Database, currently the most comprehensive repository of northern extratropical palaeo charcoal records. We examine the different geographic patterns in fire regimes across the northern extratropics from the sub-continental to circum-northern extratropical scale, by quantitatively comparing biomass burning with insolation, CO2,human population records land cover changes. This study provides novel insight into the fire regimes that have characterized the northern extratropics over the Holocene and the differential importance of environmental controls in shaping these burning histories.

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 score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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
Published2021
Admission routes1
Has abstractyes

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