Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The 14th ACM Multimedia Systems Conference (with the associated workshops: NOSSDAV 2023, MMVE 2023, and the first edition of GMSys 2023) took place from 7th - 10th June 2023 in Vancouver, Canada. The MMSys conference brings together researchers in multimedia systems to showcase and exchange their cutting-edge research findings. Once again, there were technical talks spanning various multimedia domains and inspiring keynote presentations. Participants had also the opportunity to further interact with colleagues while enjoying the sunset with a 360° view of Vancouver on the Lookout tower or during a dinner in the core of the rainforest. Additionally, this year's event included a special session dedicated to the memory of Dr. Kuan-Ta Chen, to honor his invaluable contributions to the multimedia community and to inspire the future generation of researches. To encourage junior researchers to participate on-site, SIGMM has sponsored a group of students with Student Travel Grant Awards. For many of them, this was their first time presenting at an international conference, and it was a wonderful experience. In this article, the recipients of the travel grants share their experiences at MMSys 2023.
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.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 it