Streamlining an indoor positioning architecture based on field testing in pipe spool fabrication shop
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes the implementation of an indoor positioning architecture based on radio frequency profiling using received signal strength (RSS) measurements for localizing and tracking resources in construction-related applications. The profiling-based approach is coupled with commonly used noise filtering algorithms in order to cope with the application of material tracking in a pipe spool fabrication shop. With 95% likelihood, consistent positioning accuracy of 1-2 meters away from the actual position of a tracked tag can be obtained in the fabrication shop--which is deemed sufficient for materials and labor hours tracking in support of shop production control. In particular, through simulation experiments using data collected from a pipe fabrication shop we investigated the sensitivity of the resulting localization accuracy with respect to the quantity and layout of the reference points, aimed at streamlining system updating and simplifying solution implementation.
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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 | 0.001 |
| 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 it