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Supplementary material from "Persistent positive anomalies in geopotential heights drive enhanced wildfire activity across Europe"

2025· other· en· W6959242324 on OpenAlexaboutno aff

Bibliographic record

VenueFigshare · 2025
Typeother
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenetic Mapping and Diversity in Plants and Animals
Canadian institutionsnot available
Fundersnot available
KeywordsGeopotential heightGeopotentialExtreme weatherIndex (typography)Climate change

Abstract

fetched live from OpenAlex

Persistent positive anomalies in 500 hPa geopotential height (PPAs) are upper-air circulation patterns associated with surface heatwaves, drought, and fuel aridity. We examined the association between PPA events and surface fire weather and burned area at a pan-European level. Europe-wide, extreme fire weather and wildfires were on average 3.5 and 2.3 times more likely to occur concurrently with a PPA, respectively. PPAs were associated with 45% of pan-European area burned between March and October 2001–2021, and there was a latitudinal increase in the percentage of area burned during or up to seven days following PPAs up to 63% over Northern Europe. Burned area was highest up to one week following PPA presence, and fuel moisture indices from the Canadian Fire Weather Index System lagged behind peak PPA strength, demonstrating the role of PPAs in pre-drying fuels. Our findings highlight opportunities for developing early warning systems of wildfire danger, having implications for wildfire awareness and preparedness, informing policy, and wildfire management decisions like early mobilisation and resource sharing initiatives across Europe.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.756
Threshold uncertainty score0.347

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.7560.262

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 head0.009
GPT teacher head0.231
Teacher spread0.222 · 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.

Study designNot applicable
Domainnot available
GenreOther

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 routes1
Has abstractyes

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