Welcome Message from the IEEE Cluster 2023 Posters Chair
Bibliographic record
Abstract
We are pleased to present the Posters program for this year's IEEE Cluster 2023 conference at Santa Fe, New Mexico, USA, to be held from Oct 31 - Nov 03, 2023. IEEE Cluster 2023 invited submissions from academia, laboratory, and industry professionals to present their latest research findings and work-in-progress in all aspects of cluster, cloud, and grid technologies in the form of posters in four major areas of 1) Application, Algorithms, and Libraries, 2) Architecture, Networks/Communication, and Management, 3) Programming and System Software, and 4) Data, Storage, and Visualization. The Posters Committee received 19 submissions, where each poster received a minimum of three reviews. After a thorough review process, the Posters Committee accepted 14 posters in the form of extended abstracts for publication and presentation in the poster session of the conference. A special committee selected the highest-quality student poster for the Best Student Poster Award from the three Best Student Poster Candidates. We would like to thank the submitters, authors, and members of the Posters Committee for their help in putting a strong posters program in the conference. Ahmad Afsahi Queen's University, Canada IEEE Cluster 2023 Posters Chair
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.003 |
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".