Bibliographic record
Abstract
T he issues that have been so disruptive to the motion picture and televi- sion marketplace in the past year have also had an impact on the finances and operations of SMPTE.The financial status of the Society is good, not great, but good.As companies reduce staff and dotcoms close we have seen a small erosion of individual memberships.Due to the hard work of Roy Brubaker, Sustaining Membership Chair, sustaining membership has been steadily increasing.Conference attendance in these times is a challenge.The fall 2000 conference, which had a very large turnout, was offset by a small turnout at the Advanced Motion Imaging conference last February.Engineering activities are challenged, as those involved find less time available to participate.For the Society to continue to meet the educational and engineering demands of the evolving technologies it is important that funding and support levels increase.Individual memberships are the core of the Society.The Journal, discounts on conference registration, including NAB, discounts on standards CDs and other publications, and notification of section activities are just a few of the member benefits.Access to the member section of the SMPTE web site is a new benefit that will allow access to the Journal on-line as well as many other features.
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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.022 | 0.016 |
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".