Message from the symposium chairs AHPCN 2011
Bibliographic record
Abstract
AHPCN-11 contains 26 invited papers selected from the ones submitted to the HPCC-11 main track, and thus all the papers were peer reviewed by members of the HPCC-11 program committee. The symposium covers a broad range of topics in the field of high performance computing and networking such as parallel and distributed system architectures, parallel and distributed software technologies, parallel and distributed algorithms, embedded systems; grid, cluster and peer-to-peer computing; web services and internet computing, performance evaluation and measurement, distributed systems and applications, high-performance scientific and engineering computing, database applications and data mining, biological/molecular computing, mobile computing and wireless communications; network protocols, routing, algorithms; pervasive/ubiquitous computing and intelligence; autonomic, reliability and fault-tolerance; and trust, security and privacy. We thank the authors for submitting their work and the members of the HPCC-11 program committee for managing the reviews of the symposium papers in such short time. We believe this symposium complements perfectly the topic focus of HPCC-11 and provides additional breadth and depth to the main conference. Finally, we hope you enjoy the symposium and have a fruitful meeting in Banff, Canada.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.094 | 0.075 |
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".