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Record W2113634873 · doi:10.1080/21501203.2011.584577

Fueling the future with fungal genomics

2011· article· en· W2113634873 on OpenAlex
Igor V. Grigoriev, Daniel Cullen, Stephen B. Goodwin, David S. Hibbett, Thomas W. Jeffries, Christian P. Kubicek, Cheryl R. Kuske, Jon Magnuson, Francis Martin, Joseph W. Spatafora, Adrian Tsang, Scott Baker

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

VenueMycology&#58 An International Journal on Fungal Biology · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicPlant Pathogens and Fungal Diseases
Canadian institutionsConcordia University
FundersOffice of ScienceJoint Genome InstituteU.S. Department of Energy
KeywordsGenomicsComputational biologyBiologyComputer scienceGenomeGeneticsGene

Abstract

fetched live from OpenAlex

Fungi play important roles across the range of current and future biofuel production processes. From crop/feedstock health to plant biomass saccharification, enzyme production to bioprocesses for producing ethanol, higher alcohols, or future hydrocarbon biofuels, fungi are involved. Research and development are underway to understand the underlying biological processes and improve them to make bioenergy production efficient on an industrial scale. Genomics is the foundation of the systems biology approach that is being used to accelerate the research and development efforts across the spectrum of topic areas that impact biofuels production. In this review, we discuss past, current, and future advances made possible by genomic analyses of the fungi that impact plant/feedstock health, degradation of lignocellulosic biomass, and fermentation of sugars to ethanol, hydrocarbon biofuels, and renewable chemicals.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score0.628

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 science0.0010.000
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 head0.015
GPT teacher head0.247
Teacher spread0.232 · 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