Autonomous Monitoring of River Level with Real Time Event Prediction
Bibliographic record
Abstract
Observation of water level at various river sites could provide valuable insight about probable disaster in advance to initiate disaster management protocol as early as possible. We have developed an autonomous remote river water level monitoring network with event prediction algorithm at the server while maintaining a substantially low manufacturing cost. The WSN is comprised of several chosen sites based on their statistics with intelligent sensors for water level measurement. The sensors are autonomous in nature to account for any disturbance in node environment and also within the network. The real time data are transmitted to a remote server through GPRS for further processing. Server extracts information and simulates various real time parameters such as water level rise rate, time remaining to exceed the critical level for a particular site etc. A prediction algorithm running on the server side predicts the measured level values for each node over a period of time. A prototype system is implemented with six nodes at different points and has yielded satisfactory results.
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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 | 0.000 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.003 |
| 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".