Bibliographic record
Abstract
This research was conducted by employing local black jasmine rice grown in Kampaengphet, Thailand as raw material for red wine production. The author firstly studied black jasmine rice fundamental information, black jasmine starch and liquid starch. The 2 levels suitable proportions of black jasmine starch and clean water screened from 6 levels were 1:3 and 1:5 (w/w) prior to TSS adjustment to 20 and 25 oBrix. The mixture was 10 days fermented with Saccharomyces cerevisiae yeast before conducting aging at 0-4 oC for 8 weeks. It was found the alcohol content of red wine produced from black jasmine rice was 12% after 10 days of fermentation and TSS was depleted to 7.5-9.25 oBrix as pH which insignificantly reduced from 3.17-3.12 to 3.46-3.62. Antioxidant activity comparison between before and after fermentation of black jasmine wine indicated 85.14-89.06% and reduced to 80.26-86.54% respectively, due to the fermentation and aging process which reduced average 27-41% of anthocyanin even though total phenolic was average 10-54% increased. Proportion between black jasmine rice starch and clean water contained different TSS has significantly effected to wine’s sense quality at 95% of confidence level. Black jasmine rice wine produced from the black jasmine rice to clean water proportion of 1:5 with 25 oBrix TSS adjusted has obtained highest total hedonic score in the issues of color, clarity, sweetness, bitterness and significantly differed (P<0.05). All characteristics derived score were greater than 7 (moderate satisfied) except body characteristic.
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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 | 0.000 |
| 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) | 0.001 | 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".