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Record W1493791261 · doi:10.1106/dqbt-qlpd-ckyk-3h0d

Modeling Alkaline Phosphatase Inactivation in Bovine Milk During High-Temperature Short-Time Pasteurization

2001· article· en· W1493791261 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 International · 2001
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicrobial Inactivation Methods
Canadian institutionsAgriculture and Agri-Food CanadaUniversity of Guelph
Fundersnot available
KeywordsPasteurizationChemistryAlkaline phosphataseBovine milkHeat exchangerWhole milkResidualFood scienceChromatographyEnzymeBiochemistryThermodynamicsMathematics

Abstract

fetched live from OpenAlex

Alkaline phosphatase (AP) is used as the indicator enzyme for proper pasteurization of bovine milk. Predictive modeling of AP inactivation during high-temperature short-time (HTST) pasteurization would support regulations; thus ensuring the safety of heat treated milk. Activation energy (Ea) of AP in milk was measured experimentally using the capillary tube method, and Ea was found to be 429252 J/mol. The Ea was used to develop a nonlinear model to describe the thermal inactivation of milk in a small-scale HTST pasteurizer with a plate heat exchanger. Integrated pasteurization effect (PE) was obtained at different holding temperatures (62–72°C) and holding times (3–25 s), by converting times at different temperatures in various sections of the pasteurizer to the equivalent time at the reference temperature (72°C). A nonlinear function was developed to relate the log(% residual AP activity) to PE. The r 2 varied from 0.7488 to 0.8311. The validation trial indicated that the model could predict AP activity accurately for the% residual AP activity >1%.

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 score0.008
Threshold uncertainty score0.426

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 head0.009
GPT teacher head0.260
Teacher spread0.252 · 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