Bibliographic record
Abstract
Monitoring the environment is critical for scientists to understand the environmental dynamics. However, the traditional monitoring systems such as sensor networks are usually labor-intensive and complicated to deploy. As the concept of citizen sensing has been proposed to include volunteers into the environmental monitoring systems, a key to realize the citizen sensing vision is to empower citizens with the low-cost and easy-to-use sensing devices. In this paper, we focus on two technologies that have the potential to realize the citizen sensing vision: the Internet of Things (IoT) and the world-wide sensor web. In order to address the issues from the decentralized and heterogeneous nature of IoT devices and sensors, we propose the TinySOS service, a tiny web server hosting a light-weight profile of Open Geospatial Consortium (OGC) Sensor Observation Service (SOS). By hosting open standard sensor web services on the IoT devices, not only the devices become self-describable, self-contained, and interoperable, but also the collected observations are accessible via the Internet as soon as they are measured. In this case, the sensor web can provide real-time sensor data streams in a much higher spatial and temporal resolution, which consequently allows scientists to observe phenomena that were previously unobservable.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".