Monitoring the Behaviour of a Battery-free Water Powered Sensor
Bibliographic record
Abstract
The Insurance Bureau of Canada says, “Water is the new fire”. In terms of cost of damage to homeowners and insurers, water damage is a rapidly growing threat to property and can have insidious, long-term effects if not detected early. In a study from 2018, water leaks from multiple sources are shown to be devastating to buildings. Water leaks cause a few major issues such as structural damage, mold growth, water waste. Each of these issues is minimized when water leaks are detected promptly. Water leak detectors are important devices used to detect and alert individuals or property owners about the presence of water leaks. They play a crucial role in mitigating potential water damage, reducing water waste, and minimizing associated financial losses. \nThe work herein considers a battery-free, materials-based water leak sensor. This specific sensor is unique because to date, it is the only battery-free, Bluetooth-enabled IoT sensor ready for market. A working prototype was developed in our lab, however for this sensor to work practically, the materials must be characterized and the effect of material change on electrical output must be determined. \nSince the plurality of water leak sensors available for purchase are based on closing an open electrical circuit with a conductive liquid such as water, they have wide ranges of operating temperature, humidity, and other environmental conditions. However, in the self-powered materials-based sensor, the materials themselves may react differently in different temperatures and humidity conditions. Additionally, this sensor depends heavily on a pressed-powder palette, a conductive, porous material that absorbs water. This palette is essential to the function of the sensor however, its participation in the power generation reaction is not well known.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".