Novel manufacturing methods for functional electronic textiles
Bibliographic record
Abstract
This poster introduces work on a new EPSRC project at the University of Southampton and Noƫngham Trent University developing novel manufactur-This poster introduces work on a new EPSRC project at the University of Southampton and Noƫngham Trent University developing novel manufacturing methods for funcƟonal electronic texƟles (FETT).The overall objecƟve of the research is to develop new manufacturing assembly methods that en-ing methods for funcƟonal electronic texƟles (FETT).The overall objecƟve of the research is to develop new manufacturing assembly methods that enable the reliable packaging of advanced electronic components (e.g.microcontrollers) in ultra-thin die form within a texƟle yarn.The project is invesƟ-gaƟng approaches for mounƟng the ultra-thin die onto thin flexible polymer films strips that contain paƩerned conducƟve interconnects and bond gaƟng approaches for mounƟng the ultra-thin die onto thin flexible polymer films strips that contain paƩerned conducƟve interconnects and bond pads.Individual die are located on the strip and connected via tracks to form a very thin, flexible circuit or filament.The filaments will then be surrounded by classical texƟle fibres (e.g.polyester, coƩon, wool, silk) by Noƫngham Trent University and connected via conducƟve wires to form an rounded by classical texƟle fibres (e.g.polyester, coƩon, wool, silk) by Noƫngham Trent University and connected via conducƟve wires to form an electronic yarn that will, essenƟally, appear to be a standard texƟle yarn but which has embedded within it, circuitry and sensors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".