Multi‐manifold <scp>NIRS</scp> modelling via stacked contractive auto‐encoders
Bibliographic record
Abstract
Abstract Considering different operation statuses of industrial processes, a multi‐manifold learning method that incorporates multi‐manifold assumption into near‐infrared spectroscopy (NIRS) modelling is proposed in this paper. Due to the nonlinearity, high dimension and high sensitivity problems of spectral data, the stacked contractive auto‐encoder (SCAE) is introduced to extract the multi‐manifold information from the spectral data. Then, in order to detect and evaluate the current operation status from the extracted manifold information, a heuristic criterion that compares reconstruction errors of the SCAE is proposed to evaluate the positional relation between the data point and sub‐manifold surface. By this means, ordinary least squares models are used to make appropriate predictions based on the low‐dimensional space derived from the SCAE. Finally, the NIRS data of the desalination and dehydration of crude oil are investigated to demonstrate the effectiveness and the practical application of the proposed method.
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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 | 0.001 |
| 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".