Bibliographic record
Abstract
This chapter reviews the history of biosensors, its principles, materials used, and its application in glucose monitoring and respiratory airflow monitoring. Chapter Contents: 14.1 Introduction 14.2 Monitoring principles: transducers 14.3 Diabetes and the need for glucose monitoring 14.4 Biosensor for monitoring glucose 14.5 Historical perspectives of glucose biosensors 14.5.1 First generation of glucose biosensor 14.5.2 Second generation of glucose biosensors 14.5.3 Third generation of glucose biosensors 14.5.4 Continuous glucose monitoring systems 14.5.5 Noninvasive glucose monitoring system 14.6 Respiratory airflow monitoring sensor 14.6.1 Pressure and acoustic sensing devices 14.6.2 Thermal flow sensors 14.6.3 Humidity sensors 14.6.4 CO2 sensors 14.6.5 Indirect sensors 14.6.6 Torso devices 14.6.7 Magnetometry 14.6.8 Respiratory inductance plethysmograph 14.6.9 Strain gauge 14.6.10 Transthoracic impedance plethysmograph 14.6.11 Electrocardiographic sensor 14.6.12 Electromyographic sensors 14.6.13 Photoplethysmographic sensor 14.7 Conclusion References Inspec keywords: biomedical equipment; patient monitoring; biochemistry; biosensors; health care; sugar; pneumodynamics Other keywords: glucose monitoring; healthcare; respiratory airflow monitoring; biosensors Subjects: Physical chemistry of biomolecular solutions and condensed states; Biosensors; Haemodynamics, pneumodynamics; Biomedical measurement and imaging; Biosensors; Biomedical engineering
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.009 |
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".