Comparing L1 GPS with displacement transducers and accelerometers in monitoring applications of bridges.
Bibliographic record
Abstract
The Department of Transportation of São Carlos Engineering School, Brazil, has been researching about deflections monitoring of large structures, mainly bridges, with Global Positioning System (GPS), since year 2000. This work had technical support from researches of the Timber Wood Laboratory of Sao Carlos Engineering School and researches of the Department of Geodesy and Geomatics Engineering from the University of New Brunswick, Canada. The experiments had the objective to compare the measurements of two geotechnical conventional instruments, displacement transducer and accelerometer, with L1 GPS receiver measurements. The evaluation with the displacement transducer was performed at a cable stayed timber bridge, measuring the amplitude and frequency of its dynamic displacements induced by the stepping of pedestrians. The comparison with the accelerometer consisted in applying a periodic vertical movement by means of an electro-mechanical device. GPS results were obtained by Phase Residual Method (PRM). As this method is not based on coordinate determination, it is very useful for measuring short-lived oscillations at millimeter level. The PRM is not susceptible to multipath-induced position errors (which can be up to several centimeters) and there are minimal satellite visibility constraints. By analyzing results with the different techniques, repeated tests results were obtained which indicate the precision and accuracy of GPS.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".