Modelling of inactivation kinetics of Escherichia coli and Listeria monocytogenes on grass carp treated by combining ultrasound with plasma functionalized buffer
Bibliographic record
Abstract
Linear (first-order) and non-linear (Weibull, biphasic, and log-logistic) models were evaluated for predicting the inactivation kinetics of Escherichia coli and Listeria monocytogenes on grass carp treated by a novel technique (UPFB) combining ultrasound (US) with plasma functionalized buffer (PFB). Results showed that UPFB was more effective for inactivating bacteria when compared with individual applications of US or PFB with reductions of 3.92 and 3.70 log CFU/g for Escherichia coli and Listeria monocytogenes, respectively. Compared with the linear model, the three non-linear models presented comparable performances and were more suitable for describing the inactivation kinetics with superior adj-R2 (0.962–0.999), accuracies (0.970–1.006) and bias factors (0.995–1.031), and by assessing the strengths of evidence, weights of evidence and evidence ratios for the models, the biphasic model was identified as the best fit model. The current study provided new insights into the effective evaluation of decontamination methods.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".