Guest Editorial Special Issue on Selected Papers From the IEEE Sensors 2019 Conference
Bibliographic record
Abstract
IEEE Sensors is the flagship conference of the IEEE Sensors Council, attracting each year more than 800 paper submissions, on diverse topics related to sensing technologies, sensors devices, systems, and communications. The IEEE Sensors 2019 Conference was held in Montreal, QC, Canada, on October 27–30, 2019, and featured a program with 13 regular tracks and three focused sessions. The regular tracks targeted areas such as sensor phenomenology, modeling and evaluation, sensor materials, processing and fabrication (including printing), chemical, electrochemical, and gas sensors, microfluidics and biosensors, optical sensors, physical sensors, temperature, mechanical, magnetic and other sensors, acoustic and ultrasonic sensors, sensor packaging (including flexible materials), sensor networks (including IoT and related areas), emerging sensor applications, sensor systems: signals, processing and interfaces, as well as actuators and sensor power systems. One track was dedicated specifically to sensors in industrial practices. The three focused sessions targeted areas such as engineering in medical diagnostics and therapeutics, biomedical sensors based on electromagnetics, and flexible and printed IoT sensors.
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.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.059 | 0.049 |
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".