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Record W4323304380 · doi:10.18280/ijdne.180106

A Model Study of Growth of Publications on the Field of Biofuels

2023· article· en· W4323304380 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.

venuePublished in a venue whose home country is Canada.
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

VenueInternational Journal of Design & Nature and Ecodynamics · 2023
Typearticle
Languageen
FieldMaterials Science
TopicMetallurgy and Material Science
Canadian institutionsnot available
FundersEuropean Regional Development FundJunta de ExtremaduraConsejo Nacional de Ciencia y TecnologíaEuropean Commission
KeywordsBiohydrogenBiofuelBiodieselRenewable energyBiogasEnvironmental sciencePulp and paper industryAgricultural economicsBiotechnologyWaste managementEngineeringEconomicsChemistryBiologyHydrogen production

Abstract

fetched live from OpenAlex

There is a great interest in biofuels production as an alternative and potentially renewable fuel. This study analyzed the rate of scientific publications related to the biofuels such as biodiesel, bioethanol, biogas, biohydrogen and wood pellets using the Logistic and Gompertz models to quantitatively describe the publications growth. The models showed fit to the biofuels growth data as indicated by the determination coefficient. China was the most productive country with publications of biohydrogen, bioethanol, biodiesel and biogas. Overall, research on the topic of biofuels, biohydrogen and bioethanol are increasing with a rate of publications greater than 0.26 years-1. From 2003 there was growth in the rate of publications of each biofuel evaluated in this work.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.141

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.033
GPT teacher head0.306
Teacher spread0.274 · 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