Proceedings of the Sixth International Workshop on Data Management for Sensor Networks
Bibliographic record
Abstract
Welcome to the 6th edition of the International Workshop on Data Management for Sensor Networks, a.k.a., DMSN'09! The program of this year's workshop spans a variety of themes within the DMSN domain, including systems-oriented and application-oriented approaches, coming from research teams from Europe, North America, and Asia. DMSN'09 received 16 submissions for research papers, out of those we were able to accept 6 papers, yielding a healthy acceptance rate of about 38%. In addition to the traditional track of research papers, this year we also invited submissions for demos and/or short papers. We received 4 submissions in that category, and decided to accept one of those, plus 3 papers that were originally submitted as research papers but that the Program Committee considered that they could be presented as short papers during the workshop. Despite the economically hard times we were very fortunate to count on Olsonet, Inc. (Canada), Arch Rock Corporation (USA), and the Swiss National Center for Mobile Information and Communication Systems (NCCRMICS) (Switzerland), who have kindly served as sponsors to DMSN'09. Arch Rock and NCCR-MICS have also agreed to participate at the workshop giving talks and demos (Olsonet was unfortunately unable to participate due to schedule conflicts). We are particularly pleased with this as it will provide the opportunity for close, and much needed, interaction between researchers and industry.
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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 | 0.009 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".