Effects of Genetic Variants of κ-casein and β-lactoglobulin and Heat Treatment of Milk on Cheese and Whey Compositions
Bibliographic record
Abstract
Origin of milk sample, heat treatment and cheesemakingThroughout this study, procedures for milk collection, heat treatment of milk samples prior to cheesemaking, cheesemaking techniques under laboratory conditions, collection and weighing of milk, cheese and whey samples, analysing components in the original milk and the resulting cheese and whey were the same as previously described (Choi and Ng-Kwai-Hang, 1998).Briefly, 52 lactating Holstein cows were selected to provide milk for the nine possible combinations of three κ-CN and three β-LG phenotypes.These nine different phenotype combinations will henceforth be denoted as AA/AA, AA/AB, AA/BB, AB/AA, AB/AB, AB/BB, BB/AA, BB/AB, and BB/BB to indicate the phenotypes of κ-CN and β-LG, respectively.In addition, milk containing a mixture of different phenotypes for the two proteins from the bulk tank of the Macdonald Campus farm served as controls in cheesemaking experiments.The milk samples were preheated at 30, 70, 75 and 80°C prior to cheesemaking under standard laboratory conditions (Marziali and Ng-Kwai-Hang, 1986a).After overnight pressing, the cheese curds were weighed, grated, hermetically sealed in plastic bags, and stored frozen at -10°C pending chemical analysis.Subsamples of the initial milk used and corresponding whey samples were analysed in duplicate for total solids, fat and protein. Effects of Genetic Variants of κ-casein and β-lactoglobulin and Heat Treatment of Milk on Cheese and Whey Compositions
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".