MétaCan
Menu
Back to cohort
Record W1561692667 · doi:10.1111/1541-4337.12107

Fermentation Control in Baker's Yeast Production: Mapping Patents

2014· article· en· W1561692667 on OpenAlexaff
Pierre Gélinas

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsAgriculture and Agri-Food Canada
Fundersnot available
KeywordsYeastFermentationCredibilitySugarProduction (economics)Control (management)BusinessBiotechnologyBiologyFood scienceEconomicsPolitical scienceBiochemistryLawManagementMicroeconomics

Abstract

fetched live from OpenAlex

Abstract During baker's yeast manufacturing, the fermentation process must be thoroughly controlled. This review of the patent literature provides new information on the early development of industrial fermentation processes. As shown by a review of 199 patents filed between 1900 and 2009, inventors in this field were mainly interested to improve yeast yields through the control of infection and sugar concentration in the growth media. Contrary to common belief, much attention was also given to continuous culture processes, involving addition and withdrawal of growth media. These technologies were mainly developed in about 30 y, between 1910 and 1939. In the recent years, inventors gave sustained attention to the fine tuning of fermentation control, mainly the rapid determination of yeast fermentation by‐products in the exhaust to get rapid feedback on the rate of sugar addition in the fermentation tank. Improved fermentation control benefited much the baking industry because baker's yeast had higher gassing power and was cheaper. However, some of these key patents on baker's yeast technology were later declared invalid in court because it had little intellectual property value. In the baker's yeast trade and other sectors, this situation might have encouraged trade secrets while reducing the credibility of innovative ideas disclosed in the patent literature.

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)0.001
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.914
Threshold uncertainty score0.254

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.069
GPT teacher head0.267
Teacher spread0.198 · 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 designObservational
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

Citations15
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueComprehensive Reviews in Food Science and Food SafetySame topicFermentation and Sensory AnalysisFrench-language works237,207