Predicting oxidative stability of vegetable oils using neural network system and endogenous oil components
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The usefulness of Artificial Neural Network Systems (ANNW) to predict the stability of vegetable oil based on chemical composition was evaluated. The training set, comprised of a composition of major and minor components of vegetable oil as inputs and as outputs, induction period and values of slopes for initiation and propagation, was measured by oxygen consumption. The best predictability was achieved for oils stored at 35°C with light exposure, when the major fatty acids, chlorophylls, tocopherols, tocotrienols, and metals were used as predictors. For oils stored at 65°C without light, a good predictability was obtained when composition of the major fatty acids and the amounts of tocopherols and tocotrienols were used. These results suggest that vegetable oil stability can be successfully predicted by ANNW when partial oil composition is known.
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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) | 0.000 | 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 it