Utilizing Sprouts WSN platform for equipment detection and localization in harsh environments
Bibliographic record
Abstract
This paper discusses the application of our Wireless Sensor Network platform called Sprouts, to monitor the steel shovel-teeth on shovelling equipment used in oil sands mining operations in Ft. McMurray, Alberta, Canada. If a fallen shovel-tooth reaches the rock crushers, serious damage to the crusher gears is expected. The Sprouts platform is utilized to monitor the viability of the shovel-teeth on the shovel bucket's teeth adapter. In addition, if a shovel-tooth becomes detached, Sprouts is used to estimate its location. By utilizing our Sprouts plug-and-play protocol, we implement a magnetic field and ultrasound distance sensor modules to detect the event of a fallen shovel-tooth. In addition, a wireless power transfer unit and two supplementary antennas are embedded in each shovel-tooth to aid in localizing (using trilateration) the part before it reaches the crusher.
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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 | 0.000 |
| 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".