Real-time collaborative intelligent services for sensor networks
Bibliographic record
Abstract
Sensor networks that distribute real-time GIS (Geographic Information System) data are being deployed across the world more than ever as it becomes increasingly affordable and important for scientific and government agencies to monitor environmental phenomena. As the amount of data sources increases, the lack of services for connecting users to the appropriate sensor networks becomes very apparent. The real-time collection and analysis of data from a large variety of heterogeneous sensor sources is currently difficult due to the lack of a standard architecture to expose the capabilities of a particular sensor network. What is needed is a service-oriented architecture (SOA) that allows sensor data to be exposed as collaborative services which are accessible from within a typical web-browser. We propose a solution that allows real-time access to the appropriate sensor network data for multi-user web-based collaboration via a publisher/subscriber architecture. To evaluate the proposed architecture, we show how a AJAX-based and a Flash-based web application are able to gain access to the real-time sensor data services and collaborate on the data in novel ways.
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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 | 0.004 |
| 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 | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".