Bibliographic record
Abstract
ABSTRACT On-demand webinar originally hosted by SMTA Ontario Chapter Technical Presentation: The IPC - 1782 standard defines 4 levels of traceability data collection for both Material Traceability and Process Traceability Level 1 - consists of a part number listed to a work order, and usually involves manual reporting Level 2 - incorporates unique material ID for each work order and critical process characteristics Level 3 - includes a unique Material ID assigned to each PCB assembly Level 4 - Includes a unique Material ID for all metrics on the PCB (parts) This data, as it becomes more specific, can be used reduce recall costs and improve quality. any serious product issue that occurs can be more accurately identified and thus minimize rework cost. Speaker: Michael Kou ◦ Graduated from Iowa State University with Bachelor of Science with a joint degree in Mechanical Engineering and Aerospace Engineering in 1981. ◦ Worked for Detroit diesel Allison in computational fluid dynamic for 2 years ◦ Graduated from MIT with a Masters in Mechanical Engineering (MSME) in 1985 specialized in bio-mechanic ◦ Worked for Digital Equipment Corp. in Advance Manufacturing Group for about 11 years ◦ Started Accu-Assembly Inc. in 1997 and holds a patent in Automated verification system for placement equipment On Demand Webinar
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 | 0.017 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.156 | 0.075 |
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".