Simultaneous Classification and Concentration Estimation for Electronic Nose
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
By virtue of the electronic nose (E-nose), detection and estimation of gases become feasible in many fields without resorting to complicated specific instruments. Detection is generally casted as a classification problem and concentration estimation is subsequently performed using conventional statistical techniques. In this paper, we develop a polynomial-based optimization method to perform classification and estimation simultaneously to improve the intelligence of an E-nose. The proposed method employs a parametric polynomial with user-defined order to describe sensor characteristics. Classification and concentration estimation can then be formulated as a standard convex optimization problem. The convex optimization is solved either by a typical gradient descent method for an unconstrained case or a NLS trust-region method for a constrained case. The main advantages of the proposed method are the flexibility and significant reduced computation cost as well as simple implementation. Moreover, the global minimum of the optimization is readily achieved. Experimental data analysis demonstrates the efficiency of the proposed method
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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