Bibliographic record
Abstract
technical Journal to "disseminate scientific information concerning the motion picture, television and allied arts and sciences."SMPTE has a long history of documenting development and advances in the motion imaging field.We started of course with film, then added television imaging and recording, and now new methods of both recording, file-based storage, and dissemination by means such as the internet.I am writing this from my company's offices in New York and often wonder what Thomas Edison, a motion imaging pioneer if there ever was one, would think of the new systems, methods, and the truly amazing quality we have achieved.The SMPTE Motion Imaging Journal is a peer-reviewed publication.Unlike many magazines in our industry, Journal papers are vetted by a team of industry experts for technical content.LeRoy DeMarsh, retired from Eastman Kodak, is the Chairman of our Board of Editors.Board members, whose names appear every month on the Journal's masthead page, volunteer their time to read, comment, and correct submissions, so that the Journal can maintain the highest standards.Papers selected for potential publication are reviewed by several editors, chosen for their expertise in our industry.In order to pass muster, submissions must meet requirements for interest, technical clarity, writing style, and appropriateness.Again, I'd like to thank LeRoy and his team for their work in reviewing all the articles and papers we receive.Like the U.S. Marines, the Board of Editors is always "looking for a few good men" and women.If you would like to volunteer, please contact David Juhren, our Director of Publications, (djuhren@smpte.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.008 |
| Insufficient payload (model declined to judge) | 0.096 | 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".