MétaCan
Menu
Back to cohort
Record W4221108264 · doi:10.1111/1751-7915.14050

Addressing the energy crisis: using microbes to make biofuels

2022· editorial· en· W4221108264 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMicrobial Biotechnology · 2022
Typeeditorial
Languageen
FieldEngineering
TopicBiofuel production and bioconversion
Canadian institutionsUniversity Health Network
FundersAgencia Estatal de Investigación
KeywordsRenewable energyFossil fuelBiofuelNatural resource economicsEnergy securityEnvironmental scienceProsperityRenewable fuelsGreenhouse gasBiomass (ecology)Environmental protectionBusinessWaste managementEngineeringEcologyEconomicsEconomic growthBiology

Abstract

fetched live from OpenAlex

Much of the energy being used to power our lives comes from fossil fuels such as coal, natural gas and petroleum. These energy sources are non-renewable, are being exhausted and also pollute the air, water and soil with toxic chemicals. Their mining, transportation, refining and use are associated with a large carbon footprint that contributes significantly to global warming. In addition, the geopolitical complexities surrounding the main fossil fuel producers create risks and uncertainties around the world. Replacing fossil fuels with clean, renewable forms of energy is paramount to creating a sustainable and healthy future, and for laying the foundations for global political stability and prosperity. Using biomass from plants, microbes can produce biofuels that are identical to or perform as well as fossil fuels. In addition of creating sustainable energy, advancing the biofuel industry will create new, high-quality rural jobs whilst improving energy 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.

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 categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.133
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0020.002
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 head0.022
GPT teacher head0.245
Teacher spread0.223 · 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