PreOBP_ML: Machine Learning Algorithms for Prediction of Optical Biosensor Parameters
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
To develop standard optical biosensors, the simulation procedure takes a lot of time. For reducing that enormous amount of time and effort, machine learning might be a better solution. Effective indices, core power, total power, and effective area are the most crucial parameters for evaluating optical sensors. In this study, several machine learning (ML) approaches have been applied to predict those parameters while considering the core radius, cladding radius, pitch, analyte, and wavelength as the input vectors. We have utilized least squares (LS), LASSO, Elastic-Net (ENet), and Bayesian ridge regression (BRR) to make a comparative discussion using a balanced dataset obtained with the COMSOL Multiphysics simulation tool. Furthermore, a more extensive analysis of sensitivity, power fraction, and confinement loss is also demonstrated using the predicted and simulated data. The suggested models were also examined in terms of R2-score, mean average error (MAE), and mean squared error (MSE), with all of the models having an R2-score of more than 0.99, and it was also shown that optical biosensors had a design error rate of less than 3%. This research might pave the way for machine learning-based optimization approaches to be used to improve optical biosensors.
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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