Bibliographic record
Abstract
ABSTRACT On-demand webinar originally hosted by SMTA Ontario Chapter Technical Presentation: Electromagnetic fields have become ubiquitous in our society with nearly everyone carrying something that emits a signal. As a consequence, the outside world has become a soup of these signals and there's potential for cross-talk between neighboring devices leading to malfunction. Classically, system designers could implement simple solutions such as twisted pair cabling or surround emitting devices with a metallic foil; however, modern circuitry demands more robust solutions that reduce weight, minimize cost and better conform to intricate part design. With these requirements, electrically conductive paint emerges as a practical and economical solution, requiring little expertise to apply, facilitating high throughput and quick turnaround. In this webinar, we will discuss the role of electrically conductive coatings and how they are used to attenuate external electromagnetic and radio frequency interference (EMI/RFI). Specific topics include introductory theory behind EMI attenuation, the advantage of conductive coatings over traditional materials, a comparison of the different coating technologies and how to choose the right coating system based on application requirements with reference to specific case studies. Speaker Bio: Michael Strong is the Research and Development Manager for MG Chemicals. In this role, he oversees a team of chemists develop specialty coating, resin and adhesive systems that address specific customer challenges across all industries including thermal management, circuit ruggedization and electromagnetic interference. Throughout his tenure at MG, he has worked in product formulation, quality control, technical support and as a technical marketing creator. Prior to working at MG, Michael held positions in aerospace research and medical education. He holds Masters degrees in both chemistry and business and is a certified project manager. 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".