Noninvasive detection using diffuse reflectance spectrum for monitoring jujube interior pest based on support vector machine
Bibliographic record
Abstract
Spectrum detection of jujube internal pests is used optical character of jujube, to obtain the physical and chemical information of jujube internal pests, and to establish the quantitative model by using NIR spectroscopy and chemometrics method to accurately determine the content of the material ingredients. The paper carried out second derivative to original sample data of near infrared spectrum measurement of 160 jujube samples, and selected the effective wavelengths that had the big identification capability among the wavelength range, using primary constituent analytical to reduce dimension processing. The last, the average right forecasting rate of identifying the intact and infested jujubes was about 93.5 % for the predicting set by using the algorithm of SVM, and the algorithm were proved stable. Summing up the above, the test sample could be intact, not destroyed; could determined the variety of material composition data based on simple measurement of NIR spectra for the sample at the same time; could a multi-component simultaneous determination of complex system, and could get the results of the analysis in a short time, was advantageous to the real-time industrialized production and on-line inspection, automatic classification.
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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".