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Record W2944594464 · doi:10.1111/1541-4337.12445

Active Dry Yeast: Lessons from Patents and Science

2019· article· en· W2944594464 on OpenAlexaff
Pierre Gélinas

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsCegep de Saint HyacintheAgriculture and Agri-Food Canada
Fundersnot available
KeywordsYeastProduction (economics)BiotechnologySubject matterFermentationBusinessBiologyFood sciencePolitical scienceEconomicsLaw

Abstract

fetched live from OpenAlex

In the production of fermented foods, long-term preservation of the activity of microbial starters is a critical issue. The aim of this review was to determine key challenges in the production and use of active dry yeast over time and to compare statistics on letters patent for inventions and applied scientific articles as indicators of technological evolution. The review covers 280 original patent specifications and 212 applied scientific articles issued between 1796 and 2018, not including documents in basic science or without obvious application in fermented foods. The main subject matter was baking and the other entries applied to wine and, to a lesser extent, beer and spirits. Very popular in patents granted in the 19th century until about 1935 but ignored in the scientific literature, dehydrated yeast preparations often consisted of wet biomass concentrates mixed with large amounts of water-absorbing agents. Long-term survival of dehydrated yeast cells progressively improved with specific strains, growth conditions, and, to a lesser extent, drying conditions. Since the 1990s, both inventors and scientists have mainly targeted yeast cells protection during rehydration, a most critical factor. Proper review of the scientific literature would be incomplete unless it includes patents, a much ignored but relevant source of information.

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 score0.952
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.083
GPT teacher head0.302
Teacher spread0.219 · 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

Citations33
Published2019
Admission routes1
Has abstractyes

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