Near-Infrared Spectrum of Coal Origin Identification Based on SVM Algorithm
Bibliographic record
Abstract
Near infrared spectroscopy is introduced to analyze 243 coal samples of different origins of Australia, Canada, China, Indonesia and Russia, combined with the supportive vector machines (SVM) analysis method. With the pre-processed data from the Principal component analysis (PCA), six supportive vector machines with different kernel functions are employed to discriminate origins of coal samples, namely Linear SVM, Quadratic SVM, Cubic SVM, Fine Gaussian SVM, Medium Gaussian SVM and Coarse Gaussian SVM. Through comparison, Linear SVM has the best performance in prediction accuracy rate while better results are obtained using Medium Gaussian SVM taking accuracy rate and training time into account. It turns out that NIR spectroscopy combined with Medium Gaussian SVM can be used as a good non-destructive method to predict origins of coal, with an accuracy rate of 98.8%, which strengthens the supervision of coal quality.
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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) | 0.016 | 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".