Classification of impact injury of apples using electronic nose coupled with multivariate statistical analyses
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 An electronic nose equipped with a headspace sampling unit was evaluated as a non‐destructive method for determining damage degree. Fuji apples were dropped from different heights (0.2–0.8 m) onto a cement floor inflict damages. E‐nose data was evaluated by Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) to distinguish apples based on damage severity. LDA performed better than PCA for classifying the apples. Stepwise Discriminant Analysis (SDA), Radial Basis Function Neural Network (RBFN), Multilayer Perceptron Neural Networks (MLPN), and Back‐Propagation Neural Network (BPNN) models were employed for pattern recognition. With SDA dataset, the correct classification rate (CCR) was 97.5% for training and 93.8% testing; MLPN resulted in 100%, and RFBN performed better only with more severe damages. The BPNN model had excellent correlation with classification values for damaged apples ( R 2 > 0.98). Therefore, E‐nose technology with ANN and multivariate statistics is an effective way for classifying damaged apples.
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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