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Record W2119392503 · doi:10.1071/ma11151

Systems biology: a new paradigm for industrial yeast strain development

2011· article· en· W2119392503 on OpenAlex
Cristián Varela, Simon A. Schmidt, Anthony R. Borneman, Jens O. Krömer, Alamgir Khan, Paul J. Chambers

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.

fundA Canadian funder is recorded on the work.
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

VenueMicrobiology Australia · 2011
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicFungal and yeast genetics research
Canadian institutionsnot available
FundersUniversity of New South WalesMacquarie UniversityAustralian GovernmentGovernment of South AustraliaAlberta Water Research Institute
KeywordsYeastBiochemical engineeringStrain (injury)Key (lock)BiologyIndustrial biotechnologySystems biologyComputational biologyIndustrial microbiologyBiotechnologyComputer scienceFermentationEngineeringGeneticsFood scienceEcology

Abstract

fetched live from OpenAlex

One of the key challenges for industrial yeast strain development is to obtain a thorough understanding of the biology of yeast and to apply this knowledge to develop novel strains with improved features. The detailed study of individual biological components and the use of metabolic engineering have benefited the development of industrial strains enormously; however, such approaches have failed to describe yeast behaviour in the detail required to reveal the complex interactions operating within such biological systems. How can we accurately describe the biological processes and the interactions that occur during fermentation or cell growth?

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.441
Threshold uncertainty score0.830

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 integrity0.0010.000
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 head0.166
GPT teacher head0.322
Teacher spread0.156 · 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