Artificial Intelligence Assisted Noncontact Microwave Sensor for Multivariable Biofuel Analysis
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
Multivariable component analysis is one of the most challenging topics in the area of microwave resonator based sensors. In this article, a new approach is developed for introducing new independent features for analyzing the volumetric fraction of water, ethanol, and gasoline in E85 biofuel samples. The novel features are extracted based on a multiharmonics measurement of frequency and amplitude variations of the transmission response of the resonator over multiple harmonics due to nonlinearity and uniqueness of the permittivity spectrum of different materials. For the experiments, 60 samples of biofuel mixtures are prepared with randomly chosen percentages of each of the components. An artificial neural network is trained with the extracted features from 40 of the samples and tested over the remaining 20 samples. The average relative error in determining the water concentration in the biofuel samples of as low as 0.09% is achieved. The experimental results verify the capability of the sensor for selective analysis of all the components of a multivariable mixture simultaneously.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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 it