Non-Contact Additive Processes for Contemporary Electronics Production
Bibliographic record
Abstract
ABSTRACT Co-Hosted with: iNEMI This webinar will present the opportunities for non-contact additive processes for contemporary electronics production. The presentation will highlight specific aspects of the demands of the SMT industry with respect to application specifications, such as positioning and volume. A cursory description of traditional application methods will lead into a description of contemporary non-contact deposition strategies, including jet dispensing and jet printing. More exotic technologies will also be presented. About the Presenter Gustaf Mårtensson, Ph.D., works as a Complex Fluids Expert at Mycronic AB, where he focuses on non-contact deposition technologies and novel electronic materials. He is also an affiliated researcher at the School of Chemistry, Biotechnology and Health at the Royal Institute of Technology (KTH) in Stockholm, Sweden. At KTH he works with theoretical and experimental microfluidics, specifically on clinical point-of-care applications. Gustaf has an M.Sc. in engineering physics and a Ph.D. in the area of fluid dynamics, both from KTH. Files Available to Download: Slides and Link to Recorded Presentation (PDF)
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.232 | 0.100 |
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".