Optimization of Sensor Arrays for the Identification of Abalone Flavoring Liquids
Bibliographic record
Abstract
to optimize the sensor arrays which are used to classify and identify these fluids. It preliminarily screens the response performance of single sensors in the testing system using the intra-class mean square, the F value and the P value by one-way analysis of variance (one-way ANOVA) method, and then performs significance analysis of the screened sensors according to the multiple comparison analysis method, classify them by significance and then group them into three different sensor arrays. After that, this paper performs principal component analysis and cluster analysis on the signals of each sensor array, and the results show that the optimized sensor arrays all have better performance than the ones before optimization in classification and identification of samples, and that in particular, the optimized array I consisting of Au, Pt, Pd and W has the best performance and can be applied in the classification and identification of abalone flavoring liquids.
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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 | 0.001 |
| 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".