Bibliographic record
Abstract
ABSTRACT Hosted Jointly by the Michigan, Ohio Valley, Ontario, Connecticut & Boston Chapters This webinar will go over the trends that are within the electronics industry that are driving the further penetration and expansion of Ruggedization technologies. These future looking trends will be supported by current real work impacts; where the lack of proper Ruggedization have led to failure. Along with an overall understanding of what Ruggedization is, why these processes are done, where these skills are used as well as the value proposition to be understood. These concepts will be further supported by a high-level review of the upstream processes that are needed as well as process controls that are empowered. About the Speaker: Jason Keeping is the Global Process Subject Matter Expert for Ruggedized Electronics within the Global Technology and Operations team at Celestica. He holds a B.A.Sc in Electrical Engineering from the University of Ryerson, is a Six Sigma/Lean Professional and a licensed professional engineer. Jason has focused his career on assessing the manufacturability and capabilities of conformal coating, potting, underfill chemistries & their interactions, dispensing technologies/processes and on assembly-level cleaning for the past twenty years. Jason is the recipient of several awards, including the 2016 SMTA Excellence in International Leadership Award, the Shingo prize for Celestica’s site in Monterrey, Mexico in 2007, and has contributed to Celestica’s Frost & Sullivan awards in 2005, 2009, 2012 & 2013. Jason is the co-chair of the IPC-HDBK-830 handbook; author for conformal coating chapter of the Printed Circuits Handbook & Lead-Free Soldering Process Development and Reliability as well was featured in the cover of Circuits Assembly in May 2008 and is currently the chair of the 5-30 Cleaning and Coating Committee within IPC and chairman overseeing the Canadian Aerospace Manufacturing Education program at Downsview Aerospace Park. Files Available to Download: Recorded Presentation (On-Demand)
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".