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Assessing the potential of wildfires as a sustainable bioenergy opportunity

2012· article· en· W2144962220 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueGCB Bioenergy · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
FundersCYTED Ciencia y Tecnología para el DesarrolloInternational Development Research CentreInter-American Institute for Global Change ResearchNational Science Foundation
KeywordsBioenergyGreenhouse gasEnvironmental scienceFossil fuelBiomass (ecology)Natural resource economicsBiofuelLand useAgroforestryEnvironmental protectionEcologyEconomics

Abstract

fetched live from OpenAlex

Abstract As the environmental and economic consequences of fossil‐fuel use become clear, land is increasingly targeted as a source of bioenergy. We explore the potential for generating electricity from biomass vulnerable to fires as an ecologic and socioeconomic opportunity that can reduce the risk of greenhouse gas generation from wildfires and help to create incentives to preserve natural and seminatural vegetation and prevent its conversion to agriculture, including biofuel crops. On the basis of a global analysis of the energy generation and spatial distribution of fires, we show that between 2003 and 2010, global fires consumed ~8300 ± 592 PJ yr −1 of energy, equivalent to ~36–44% of the global electricity consumption in 2008 and >100% national consumption in 57 countries. Forests/woodlands, cultivated areas, shrublands, and grasslands contributed 53%, 19%, 16%, and 3.5% of the global energy released by fires. Although many agroecological, socioeconomic, and engineering challenges need to be overcome before diverting the energy lost in fires into more useable forms, done cautiously it could reconcile habitat preservation with economic yields in natural systems.

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.

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 categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.354
Threshold uncertainty score0.999

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 studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.011
GPT teacher head0.250
Teacher spread0.239 · 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