The Product Acceptance Preferences of Gayo Arabica Coffee Brewing with Additional Fruit and Spices Variants
Bibliographic record
Abstract
There have been significant changes to coffee-drinking cultures worldwide regarding how and where to consume coffee. The increase in coffee enthusiasts, how to enjoy coffee is also growing, starting from the addition of milk, chocolate, and various kinds of mixtures that follow existing local trends and wisdom. The purpose of this study is to analyze the product acceptance preferences of Gayo arabica coffee brewing with additional fruit and spices variants using the Simple Additive Weighting (SAW) method. In this study, there were 5 products analyzed, namely a mixture of espresso with sap (nirapresso), a mixture of espresso with sweet orange (orangepresso), a mixture of espresso with lime (limaupresso), a mixture of espresso with lemongrass (serehpresso) and a mixture of espresso with nutmeg (palapresso). The espresso used in this study was 30 ml, with the addition of fruit and spice extracts of 30 ml, 45 ml, and 60 ml for each product. The results showed that the panellists had different preferences for each alternative type of brewing. The alternatives of each product were obtained that nirapresso had the highest acceptance preference for the addition of 45 ml of sap, orangepresso with the addition of 60 ml of sweet orange, lime with the addition of 60 ml of lime, lemongrass with the addition of 60 ml of lemongrass, and palapresso with the addition of 30 ml of nutmeg.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".