A framework for indoor construction resources tracking by applying wireless sensor networks<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.
Bibliographic record
Abstract
The state-of-the-art tracking technologies, such as the global positioning system (GPS) and the radio frequency identification (RFID), lend themselves well to applications in relatively open areas, while falling short of accuracy and reliability in indoor or partially covered application settings due to signal blockage, distortion or deterioration. This research aims to address this challenge in construction engineering by exploring a cost-effective positioning methodology to realize automated and continuous tracking of construction resources. The emerging ZigBee-based wireless sensor networks (WSN) technology is introduced. A framework of WSN application is proposed for indoor construction resources tracking, which consists of a group of stationary and mobile sensor nodes that can communicate with one another. Real-time locations of the mobile nodes can be determined by applying the localization method based on received signal strength indicator (RSSI) and geometric trilateration.
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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 | 0.001 | 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".