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Record W2058322424 · doi:10.4161/bbug.1.5.12941

Engineering organisms for industrial fuel production

2010· article· en· W2058322424 on OpenAlexaff
David A. Berry

Bibliographic record

VenueBioengineered Bugs · 2010
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Metabolic Engineering and Bioproduction
Canadian institutionsTellabs (Canada)
Fundersnot available
KeywordsRaw materialRenewable fuelsRenewable energyFossil fuelBiochemical engineeringProduction (economics)PetroleumEnvironmental scienceWaste managementEngineeringEconomicsChemistry

Abstract

fetched live from OpenAlex

Volatile fuel costs, the need to reduce greenhouse gas emissions and fuel security concerns are driving efforts to produce sustainable renewable fuels and chemicals. Petroleum comes from sunlight, CO(2) and water converted via a biological intermediate into fuel over a several million year timescale. It stands to reason that using biology to short-circuit this time cycle offers an attractive alternative--but only with relevant products at or below market prices. The state of the art of biological engineering over the past five years has progressed to allow for market needs to drive innovation rather than trying to adapt existing approaches to the market. This report describes two innovations using synthetic biology to dis-intermediate fuel production. LS9 is developing a means to convert biological intermediates such as cellulosic hydrolysates into drop-in hydrocarbon product replacements such as diesel. Joule Unlimited is pioneering approaches to eliminate feedstock dependency by efficiently capturing sunlight, CO(2) and water to produce fuels and chemicals. The innovations behind these companies are built with the market in mind, focused on low cost biosynthesis of existing products of the petroleum industry. Through successful deployment of technologies such as those behind LS9 and Joule Unlimited, alternative sources of petroleum products will mitigate many of the issues faced with our petroleum-based economy.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.281
Threshold uncertainty score0.723

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.204
Teacher spread0.195 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2010
Admission routes1
Has abstractyes

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