Machine Learning Used to Create a Multidimensional Calibration Space for Sensing and Biosensing Data
Bibliographic record
Abstract
Abstract Calibration curves are essential constructs in analytical chemistry to determine parameters of sensing performance. In the classification of sensing data of complex samples without a clear dependence on a given analyte, however, establishing a calibration curve is not possible. In this paper we introduce the concept of a multidimensional calibration space, which could serve as reference to classify any unknown sample as in determining an analyte concentration from a calibration curve. This calibration space is defined from a set of rules generated using a machine learning method based on trees applied to the dataset. The number of attributes employed in the rules defines the dimension of the calibration space and is established to warrant full coverage of the dataset. We demonstrate the calibration space concept with impedance spectroscopy data from sensors, biosensors and an e-tongue, but the concept can be extended to any type of sensing data and classification task. Using the calibration space should allow for the correct classification of unknown samples, provided that the data used to generate rules via machine learning can cover the whole range of sensing measurements. Furthermore, an inspection in the rules can assist in the design of sensing systems for optimized performance.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".