A Standardized Method for the Instrumental Determination of Cooked Spaghetti Firmness
Bibliographic record
Abstract
ABSTRACT A standardized method to determine cooked spaghetti firmness was developed. The effects of process and instrument variables were investigated and optimized to provide reproducible results between laboratories and to enable discrimination among samples with similar firmness characteristics. Commercial spaghetti samples of varying thickness were chosen to artificially create a range in firmness, and used to investigate the effect of a wide range of variables on cooked spaghetti firmness including sample preparation, cooking procedure, postcooking treatment, sample presentation, and instrument settings. Cooked spaghetti firmness determined using a TA‐XT2 i texture analyzer was significantly affected by optimum cook time, postcook cooling, rest time, and crosshead speed ( P < 0.001), as well as strand length, spaghetti to cooking water ratio, number of strands cut, and strand position ( P < 0.05). Although previous work showed a reasonable correlation between laboratories when using in‐house methods ( r = 0.85), the correlation improved to r = 0.96 when using the standardized method to analyze 29 commercially produced spaghetti samples. The Spearman rank correlation increased from r s = 0.81 to r s = 0.95, prestandardization and poststandardization, indicating greater agreement between laboratories in sample ranking.
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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) | 0.003 | 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".