Predicting safety and quality of thermally processed canned foods using a neural network
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
An artificial neural network (ANN) for reliably predicting the process temperature (T e ) and process time (t) for minimum quality degradation (F oq ) during thermal processing of canned foods was developed. Five inputs (can size, initial temperature, thermal diffusivity, sensitivity indicator of micro-organism and sensitivity indicator of quality) were used to predict the process variables T e , t, and F oq . A measure of dependency and statistical tests were used to reduce the number of inputs with little degradation of ANN performance. The feedforward ANN showed satisfactory prediction error. The mean relative error (MRE) was 0.2% in predicting T e , 3.9% in predicting t, and 1.5% in predicting F oq . The ANN showed high MRE in predicting the outputs when tested with the radial basis function (RBF) network.
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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 | 0.001 | 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