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Record W2593493978 · doi:10.1111/1541-4337.12256

Compressed Baker's Yeast: Mapping Patents on Post‐Fermentation Processes

2017· article· en· W2593493978 on OpenAlexaff
Pierre Gélinas

Bibliographic record

VenueComprehensive Reviews in Food Science and Food Safety · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicFermentation and Sensory Analysis
Canadian institutionsCegep de Saint HyacintheAgriculture and Agri-Food Canada
Fundersnot available
KeywordsYeastFermentationDewateringPulp and paper industryFood scienceMoistureEnvironmental scienceBiotechnologyProcess engineeringWaste managementChemistryBiologyMaterials scienceEngineeringComposite materialBiochemistry

Abstract

fetched live from OpenAlex

In the baking industry, fresh yeast is generally available as a compressed paste containing about 70% moisture. While much literature has been published on its growth conditions in fermentation tanks, little information may be obtained on the final steps of baker's yeast production. In this review, 226 patents were found on the separation of yeast cells from spent growth media as well as its dewatering, forming, and packaging in the compressed form. The latter corresponded to 21% of 1096 unique families of patented inventions filed worldwide between 1787 and 2014 on the production of fresh baker's yeast. Patent specifications disclosed key technical information not available in scientific journals. Particularly between 1890 and 1960, highly specific equipment such as centrifugal separators, rotary vacuum filters, and extruding and cutting units were proposed. Compressed yeast may contain small amounts of processing aids, including water-absorbing agents such as starch and salt, as well as plasticizing and whitening agents. Any variation in yeast solids and moisture contents will affect its gassing power and keeping properties. Disposal and proper treatment of very large volumes of spent growth media will remain a major challenge to the baker's yeast industry.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.957
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.123
GPT teacher head0.306
Teacher spread0.183 · 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

Citations12
Published2017
Admission routes1
Has abstractyes

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