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Medical Applications of Fermentation Technology

2013· article· en· W2086520510 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

VenueAdvanced materials research · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicViral Infectious Diseases and Gene Expression in Insects
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFermentationBiopharmaceuticalBiotechnologyYeastIndustrial fermentationBiochemical engineeringDownstream processingChemistryFood scienceBiochemistryBiologyEngineering

Abstract

fetched live from OpenAlex

Fermentation, a process traditionally known for the anaerobic conversion of sugar to carbon dioxide and alcohol by yeast, now refers to an industrial process of manufacturing a wide variety of metabolites and biomaterials by using microorganisms or mammalian cells in a controlled culture environment. Fermentation can be performed in batch mode, continuous mode or in a combinatory, fed-batch mode, depending on the product of interest. Fermentation technology has long been known for the production of various medically important products such as antibiotics, solvents such as ethanol, intermediary compounds such as citric acid, probiotics such as yoghurt etc. New generation fermentation products include anti-viral drugs, therapeutic recombinant proteins and DNA, and monoclonal antibodies. Apart from the drugs, fermentation is also used for the commercial production of materials required for the development of diagnostic kits, drug delivery vehicles and medical devices. Fermentation technology remains at the heart of rapidly growing biopharmaceutical industry today, which is expected to expand even more in the days ahead, in parallel with the progress in novel, targeted drug discovery.

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 categoriesInsufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

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)0.0010.000

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 head0.014
GPT teacher head0.373
Teacher spread0.360 · 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