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Record W3154285000 · doi:10.5194/egusphere-egu21-6404

Intensifying fire regimes in the arctic-boreal zone: recent changes, global implications, and possible solutions

2021· article· en· W3154285000 on OpenAlexaff
Brendan M. Rogers, Molly Elder, Peter C. Frumhoff, Thomas Gasser, Elena A. Kukavskaya, Erin MacDonald, Michelle C. Mack, Susan M. Natali, C. Robert Phillips, Rebecca C. Scholten, Rachael Treharne, Sander Veraverbeke, Xanthe J. Walker

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsPacific Institute for Climate SolutionsUniversity of Victoria
Fundersnot available
KeywordsTundraEnvironmental scienceBorealFire regimePermafrostGreenhouse gasTaigaClimate changeArcticGlobal warmingClimatologyLatitudeEcosystemAtmospheric sciencesPhysical geographyGeographyEcologyForestryGeology

Abstract

fetched live from OpenAlex

Across much of the high latitudes, wildfires have been increasing in frequency, area burned, and severity in response to longer fire seasons, more severe fire weather, and increased ignitions. These fires not only affect the tundra and boreal forests they burn, but also global climate due to the high levels of carbon emitted during combustion that take decades to re-aggrade. Carbon emissions from high latitude fires are generally not included in global models that inform policy nor emissions reductions commitments from relevant countries. In this presentation we describe recent progress and critical unknowns related to intensifying fire regimes in high latitude ecosystems, with a particular focus on (i) trends in burned area and large fire years; (ii) changing ignitions sources including lightning, human, and overwintering fires; (iii) patterns and drivers of carbon emissions, including interactions with permafrost; (iv) implications for global carbon budgets; and (v) potential climate mitigation through increased resources for carbon-focused fire management.

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)0.001
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: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
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 head0.022
GPT teacher head0.252
Teacher spread0.230 · 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
GenreReview

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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