Circuits evening panel discussion 2: Wearable electronics: Still an oasis or just a mirage for the semiconductor industry?
Bibliographic record
Abstract
Wearable devices have been on the mind of the semiconductor industry for several years now. The vision was of a highly sensorized human, with electronic devices providing localized, always-available functionality for a range of applications, including medical, health and wellness, lifestyle, etc. But, has this vision panned out? Is the technology there? Was there any application-level value to begin with? What is the relationship with broader sensing visions (e.g., IoT)? The collective wisdom of industrial and academic research over the last five years has made some progress in these areas — what have we learned? and how might the vision be revised? This session brings together panelists representing commercialization, emerging technologies, medical applications, and broader questions facing the domain of wearable electronics to tackle some of these questions.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.026 | 0.024 |
| Insufficient payload (model declined to judge) | 0.055 | 0.018 |
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".