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Record W2278123288 · doi:10.1007/978-1-59259-837-3_98

International Energy Agency—Bioenergy

2004· book-chapter· en· W2278123288 on OpenAlexaff
Warren Mabee, David J. Gregg, John N. Saddler

Bibliographic record

VenueHumana Press eBooks · 2004
Typebook-chapter
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsCommercializationBioenergyAgency (philosophy)Software deploymentBusinessTask (project management)Session (web analytics)Energy policyEnvironmental economicsBiofuelEngineeringMarketingEconomicsRenewable energyWaste managementSystems engineeringAdvertising

Abstract

fetched live from OpenAlex

The International Energy Agency (IEA) was founded in 1974 as an autonomous body within the Organization for Economic Co-operation and Development to implement an international energy program in response to the oil shocks. IEA Bioenergy was created in 1978 by the parent organization with the aim of improving cooperation and information exchange between countries that have national programs in bioenergy research, development, and deployment. The goal of IEA Bioenergy Task 39, “Liquid Biofuels,” is to successfully introduce biofuels for transportation into the marketplace. To meet this objective, the members of Task 39 have taken on the job of reviewing both technical and policy or regulatory issues that are related to commercializing the technology for fuel ethanol production. It is our belief that these issues are strongly related, and that successful commercialization of the technology will require advances in both areas. In this session, the Task 39 group worked to create a forum in which the interrelated issues of policy and technology could be reviewed and explored.

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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.147
Threshold uncertainty score0.493

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.005
Science and technology studies0.0010.001
Scholarly communication0.0040.003
Open science0.0010.001
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.1470.166

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 head0.033
GPT teacher head0.211
Teacher spread0.178 · 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 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

Citations13
Published2004
Admission routes1
Has abstractyes

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