Variations of Moisture Measurements in Cheese
Bibliographic record
Abstract
Data were accumulated during interlaboratory trials for cheese moisture determination from laboratories using officially recognized methods: AOAC; International Dairy Federation, and Standard Methods for the Examination of Dairy Products (SM). In one trial, ranges of means of 5 cheeses were 0.67, 0.56, and 0.19% for 5, 9, and 8 laboratories, respectively. The lower ranges for the SM method were typical of 3 other interlaboratory trials, with ranges of 0.27, 0.34, and 0.34% for 6, 7, and 5 laboratories, respectively. Within one laboratory, there were no significant differences among the 3 methods, but they all gave about 0.2% lower results than 2 other methods, one using freeze-drying, followed by drying in a vacuum, the other using cheese that was spread on sand and dried in a vacuum oven for 24 h. This finding indicated that none of the officially recognized methods removed all the moisture. Data showed that many laboratories tended to give either higher or lower results than the mean of all of them in a series of 7 interlaboratory trials. Constant results, free of biases or systematic errors, are important in application of formulas for prediction of yield of cheese for purposes of yield control, but are difficult to obtain. It is proposed that results by a laboratory in interlaboratory trials be compared with those obtained by one or more reference laboratories using a method that removes all the moisture from cheese. The difference would be applied as a constant in the predictive yield formula. That difference would likely be best as a running mean of differences in an ongoing series of trials. The reference laboratories would use frozen samples for quality control to ensure uniformity of results among trials. Mean moistures of 36.10 and 36.11% were obtained on subsamples before and after freezing for 7 months.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".