Bibliographic record
Abstract
Global positioning system (GPS) technology holds great promise as a useful tool for high-precision, real-time deformation monitoring. This article describes a demonstration project that used GPS for structural health monitoring. In the project, a GPS monitoring system was installed on the Sunshine Skyway Bridge in Tampa Bay, Florida. GPS receivers were placed at the top of the two bridge towers and at the midpoint of the center span; a fixed GPS reference site was established on the shore several miles away. Data from all four sites were collected at a one-second rate. Six months of these data were processed and analyzed. Findings indicate that the positions of autonomous sites located on a bridge can be measured automatically using GPS in near real-time, with centimeter-level accuracies. Fifteen-minute measurements provide sufficient accuracy to reveal a complex variety of motions at each point monitored on the bridge. The GPS results were used to refine a simple numerical model capable of predicting the motion of the bridge under a variety of environmental conditions. This model was used to help interpret the GPS results and identify the common natural phenomenon causing the bridge motions. The GPS observations could be used to refine and evaluate a more sophisticated model that continuously and automatically searches for deviations from expected behavior.
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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".