Artificial Neural Network-Based Predictive Model for Bacterial Growth in a Simulated Medium of Modified-Atmosphere-Packed Cooked Meat Products
Bibliographic record
Abstract
The data of Devilieghere et al. (Int. J. Food Microbiol. 1999, 46, 57--70) on bacterial growth in a simulated medium of modified-atmosphere-packed cooked meat products was processed for estimating maximum specific growth rate mu(max) and lag phase lambda of Lactobacillus sake using artificial neural networks-based model (ANNM) computation. The comparison between ANNM and response surface methodology (RSM) model showed that the accuracy of ANNM prediction was higher than that of RSM. Two-dimensional and three-dimensional plots of the response surfaces revealed that the relationships of water activity a(w), temperature T, and dissolved CO(2) concentration with mu(max) and lambda were complicated, not just linear or second-order relations. Furthermore, it was possible to compute the sensitivity of the model outputs against each input parameter by using ANNM. The results showed that mu(max) was most sensitive to a(w), T, and dissolved CO(2) in this order; whereas lambda was sensitive to T the most, followed by a(w), and dissolved CO(2) concentrations.
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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 | 0.000 | 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 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".