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Record W4296455961 · doi:10.1038/s43247-022-00522-6

Declining severe fire activity on managed lands in Equatorial Asia

2022· article· en· W4296455961 on OpenAlexaff
Sean Sloan, Bruno Locatelli, Niels Andela, Megan E. Cattau, David Gaveau, Luca Tacconi

Bibliographic record

VenueCommunications Earth & Environment · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsVancouver Island University
FundersAustralian Centre for International Agricultural Research
KeywordsGeographyPrecipitationCroppingAgriculturePercentileChinaEnvironmental scienceMeteorology

Abstract

fetched live from OpenAlex

Abstract Fire activity is declining globally due to intensifying land management, but trends remain uncertain for the humid tropics, particularly Equatorial Asia. Here, we report that rates of fire events deemed severe (≥75 th severity percentile of 2002-2019) and very severe (≥90 th percentile) for Indonesia declined 19-27% and 23-34% over 2002-2019, respectively, controlling for precipitation, where fire-event severity is given by total fire radiative power and duration. The severity of seasonal fire activity – a measure of extremeness – declined 16% in Sumatra and moderately elsewhere. Declines concentrated over mosaic croplands and nearby forest, accounting for one-fifth and one-quarter of fire activity, respectively, with each class contracting 11% amongst severe fire events. Declines were limited over mosaic lands with relatively limited cropping, despite accounting for a similar extent and one-fifth share of fire activity. Declines had an uncertain association with agricultural development but seemingly reflect related political and economic forces for economic and environmental security.

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)0.001
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 score0.309
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.021
GPT teacher head0.239
Teacher spread0.218 · 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

Citations17
Published2022
Admission routes1
Has abstractyes

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