Applications of Optoelectronics Sensor Technology in Environmental and Personal Health Monitoring
Bibliographic record
Abstract
Integrated optoelectronics is originally developed based on microelectronics processing, fabrication, and integration. Over the past decade, its focus has been shifted from telecommunication devices towards biomedical and environmental applications. The most notable advances include nano/micro-optical-electrical-mechanical systems (N/MOEMS) and optofluidics. Current technology developments not only lead to novel devices, but application oriented, full systems integration dissimilar materials and modules at the system level. These enabling technologies, in turn, facilitated the emerging growth of a number of new frontiers in biomedical, clinical, and environmental applications. Highly integrated micro-nano-systems (MNS) technology will lead to wireless sensing devices that can fit into small spaces with improved sensitivity and specificity. As a result, a number of such technologies have find application in the emerging field of environment pollutant monitoring, wearable physiological monitoring, activity sensing, and even clinical diagnostic devices. Another important feature of microelectronics fabrication based micro-nano system technology is the potential for low cost, sensitive, and automated sample handling capability. These advantages have enabled large scale, distributed sensing and monitoring applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".