Effect of preparation method on the glycaemic index of novel potato clones
Bibliographic record
Abstract
The purpose of this study was to investigate whether the effects of cooling and reheating on the glycaemic index (GI) of novel potato clones (selections) differed depending on selection and whether cooling altered starch absorption in vivo. We conducted 3 experiments using 4 novel potato clones in healthy subjects. Experiment 1: the GI of 4 selections each prepared in 3 ways (freshly boiled, cooled, or cooled and reheated) was measured in 2 groups of 10 subjects (each group tested 2 selections). Experiment 2 (n=10): two selections from Experiment 1 were re-tested one year later, by a different subject group. Experiment 3 (n=10): two selections from Experiment 1 were tested by subjects from Experiment 2 to assess the rate and extent of starch absorption using the second-meal effect and the breath hydrogen method, respectively. Experiment 1 demonstrated a selection×treatment interaction for GI (p=0.024); cooling reduced the GI of two selections by 40-50% (p<0.05) but reduced GI of the other 2 by only 8-10% (ns). Experiment 2 confirmed the selection×treatment interaction (p=0.018) seen in Experiment 1. Experiment 3: cooling reduced the GI by an average of 37% (p<0.05) but only increased starch malabsorption in vivo from 3% to 5% (p=0.021); there was no significant second-meal effect. It is concluded that the effect of cooling on the GI of potatoes may vary from 0-50% depending on selection. However, the mechanism for the effect is not clear: the 2% increase in starch malabsorption seen upon cooling potatoes was not nearly enough to account for the 37% reduction in GI.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".