From the Top: Remarkable Accomplishments as We Advance Our Initiatives Across the Globe
Bibliographic record
Abstract
Welcome, colleagues, to the summer edition of the Journal. The work the Sections and Student Chapters have accomplished in the past three months has been extraordinary. The Society has either participated in or facilitated conferences, summits, seminars, and webinars all over the globe. From Australia to the U.K. to Canada and the U.S., SMPTE volunteers have made an impact throughout the industry. Whether it was the Standards Community's Technology Committee meetings in Ottawa, the Hollywood Section participating in AfroAnimation, the Montreal Section conducting another successful boot camp, the Toronto Section's annual barbeque and conference, the U.K. Section's Media Technology Conference, or the Washington D.C. Section bringing back “Bits by the Bay” and honoring its founder Peter Wharton our Society is working to provide invaluable service to the industry worldwide. I really want to extend a special thanks to all the volunteer members who worked tirelessly to coordinate and make these events successful. Your hard work and dedication are truly appreciated.
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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 | 0.016 | 0.040 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.041 | 0.020 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.053 | 0.044 |
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".