Analysis on the Quality Change of Tempeh, Catfish and Fried Chicken as the Effect of the Repetitive Used Cooking Oil
Bibliographic record
Abstract
<p>The aim of this study is to analyze the change of product characteristic that will be fried using the cooking oil that is repetitively used by the sidewalk vendors (locally abbreviated as PKL). In this study, <em>Randomized Complete Block Design</em> (RCBD) was used and divided into 2 factors: types of product consisting of 3 levels (fried chicken, fried catfish and fried <em>tempeh</em> (Indonesian dish made of deep-fried fermented soya beans)) and frying frequency (control, 4<sup>th</sup> frying, 6<sup>th</sup> frying and 8<sup>th</sup> frying). Once obtained, the data would be analyzed using ANOVA (Analysis of variance) method in which if an interaction is found, it would be continued by DMRT (Duncan’s Multiple Range Test) using confidence interval at 5%. The result of the research shows that the types of product and the usage of repetitively used cooking oil at the different frequencies will bring an effect on the product quality after a repetitive frying by causing a significantly different impact (? = 0. 05) on water level, nutrient level, peroxide number, TBA level, p-Anisidine number at 0, 38, and free fatty acid levels.</p>
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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 | 0.008 | 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 | 0.000 |
| 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 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".