Proceedings of the 2nd ACM annual international workshop on Mission-oriented wireless sensor networking
Bibliographic record
Abstract
It is our great pleasure to welcome you to The Second ACM Annual International Workshop on Mission-Oriented Wireless Sensor Networking -- ACM MiSeNet'13. The second edition of this year's workshop is the premier forum for presentation of research results and experience reports on leading edge issues of mission-oriented wireless sensor networks, including models, systems, applications, and theory. The mission of the workshop is to understand the major technical and application challenges as well as exchange and discuss scientific and engineering ideas related to architecture, protocols, algorithms, and application design in mission-oriented wireless sensor networks, and identify new directions for future research and development. ACM MiSeNet gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of mission-oriented wireless sensor networking. The call for papers attracted 12 submissions from Africa, Asia, Canada, Europe, and the United States. The program committee accepted 8 papers that cover a variety of topics, including data delivery in vehicular networking, data aggregation, quality of event assessment, human-machine interactions, graph-based modeling, reachability verification, indoor location fingerprints, and queuing modeling for delay analysis in missionoriented sensor networks. In addition, the program includes a keynote speech by Prof. Jie Wu on the trajectory optimization for mobile chargers in wireless sensor networks. We hope that these proceedings will serve as a valuable reference for researchers and developers in the area of mission-oriented wireless sensor networking.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.029 | 0.011 |
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".