Guest Editorial for Special Issue on Papers From 2023 IEEE International Conference on Flexible Printable Sensors and Systems (FLEPS)
Bibliographic record
Abstract
This Special Issue of the IEEE Journal on Flexible Electronics (J-FLEX) showcases the expanded papers presented at the 2023 IEEE International Conference on Flexible Printable Sensors and Systems (FLEPS), held in Boston, MA, USA. FLEPS was organized by the IEEE Sensors Council. The advancements in flexibility and printability are reshaping the landscape of electronic device design and production, sparking great enthusiasm globally for the field of flexible and printable electronics. FLEPS served as an excellent platform for engaging in discussions about the latest advancements in the field and shaping future pathways for sensors utilizing unconventional materials and manufacturing technologies. FLEPS garnered an enthusiastic response, attracting leading experts, researchers, and innovators from both academia and industry. The technical program of FLEPS 2023 comprised over 150 presentations spanning three days. Submitted papers underwent a rigorous peer-review process. The authors whose papers were accepted were encouraged to submit extended versions for consideration, hence the creation of this special issue.
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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 0.043 |
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".