Proceedings of the sixteenth annual international conference on Mobile computing and networking
Bibliographic record
Abstract
On behalf of the organizing committee, I welcome you to the 2010 ACM MobiCom and MobiHoc. These two conferences were brought together for the first time in 2007 in Montreal. The 2010 edition is the sixteenth in its series for MobiCom and eleventh for MobiHoc. Over the years, both conferences have established themselves as premier forums for presentation of research on mobile computing and wireless networking. I hope that you will attend the exciting paper presentations at both the conferences, which are being scheduled as parallel tracks this year. The papers included in the conference proceedings reflect the outstanding research performed by our authors, and also the conscientious and dedicated efforts of the two technical program committees, ably led by Suman Banerjee and Dina Katabi for ACM MobiCom, and Christoph Lindemann and Jitendra Padhye for ACM MobiHoc. The program committee chairs devoted substantial effort, working with the respective program committees, to ensure that the review process resulted in fair and timely decisions. In addition to the refereed papers, this year's program also includes poster and demo sessions. I am sure that you will enjoy the diversity of research being presented in these sessions. We also have several workshops scheduled this year on a wide range of emerging topics of interest to the conference attendees.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.096 | 0.074 |
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".