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Record W2995804955 · doi:10.1590/fst.13119

Pasteurization effects on yield and physicochemical parameters of cheese in cow and goat milk

2019· article· en· W2995804955 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

VenueFood Science and Technology · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicProbiotics and Fermented Foods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPasteurizationRaw milkFarmer cheeseFood scienceCow milkRaw materialYield (engineering)Milk productionToned milkMilk productsChemistryBiologyAnimal scienceMaterials science

Abstract

fetched live from OpenAlex

Cheese production is one of the most common forms of valorization of dairy production, adding value and preserving milk. Various types of cheese produced from raw and pasteurized milk are known worldwide. In the present work, we are interested in studying the effect of the type of milk (raw and pasteurized) of two species (cow and goat) on the yield and physicochemical characteristics of the fresh cheese. In the northeastern Algeria, on 5 cow farms and 3 goat farms; 5 raw and 5 pasteurized milk cheese manufacturing trials were conducted. The analysis of the results of the 80 samples of milk and cheese of both cow and goat species showed that the latter contained significantly more fat and protein than cow's milk and that pasteurized milk contained more protein than raw milk. As a result, actual cheese yield of goat cheese was higher than that of cows in pasteurized and raw milk. For higher yield, our result supported the use of pasteurized milk as a raw material in the manufacture of farmhouse cheese.

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 score0.117
Threshold uncertainty score0.123

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 studies0.0000.000
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 head0.008
GPT teacher head0.191
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