Evaluation of Quartz Piezoelectric Weigh-in-Motion Sensors
Bibliographic record
Abstract
Quartz piezoelectric weigh-in-motion (WIM) sensors are potentially appealing because of their low sensitivity to temperature fluctuations, according to information gathered from previous installations in other states. These installations also called into question the durability of the quartz sensors. Some sensors stopped producing a signal even though the sensor installation did not exhibit physical distress. After design changes were made, it was necessary to reevaluate the quartz sensor before any major investment in this technology in Texas. This research was undertaken to evaluate the performance and durability of quartz piezoelectric WIM sensors. To perform this evaluation, researchers instrumented two sites with quartz sensors and collected truck weight and sensor condition data to analyze the accuracy and durability of these sensors. The results of this research show that the sensors meet or exceed the weight accuracy specified by the ASTM specification for Type 1 highway WIM systems. The data further show that the truck weights produced by the WIM system are stable over time with minimal variation due to temperature change. There have been no sensor failures or degradation of the installations to date.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 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".