A comparison of two instrumental techniques used to discriminate the cooking quality of spaghetti
Why this work is in the frame
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Bibliographic record
Abstract
Summary Pasta is a popular food whose quality can be measured by appearance, flavour and texture. Several instruments have been devised to measure texture but there is little comparative information. This study compared the TA.XT2i texture analyser with the viscoelastograph of thirty spaghetti samples. There was a high correlation between these instruments and good agreement in ranks. While both instruments provide comparable data it is not the same. Two laboratories used the texture analyser to measure cooked spaghetti firmness using their own procedures. There was good agreement in firmness, however; there were differences in the ranks for samples that fell between the extremes in firmness. We attributed these differences to variations in the instrument settings, cooking method and sample presentation used by the laboratories indicating the need to standardise the method. Using a standard method greatly improved the correspondence between the laboratories improving the r 2 to 0.99 with excellent agreement in the ranking of ten samples.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it