Generating Bridge Structure Model Details by Fusing GIS Source Data Using Semantic Web Technology
Why this work is in the frame
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Bibliographic record
Abstract
Many parameter values needed for creating high fidelity 3D models of components above and below the terrain of a region may not be explicitly present in the GIS source data gathered for that region, but may be implicit in the combined knowledge in these multiple types of data sets. Hence considerable effort from GIS experts is often involved in the creation of high fidelity 3D models. In this paper, we propose a Data Extractors framework which fuses data from shape file, elevation, and imagery datasets and automatically derives specific parameter values needed for creating 3D models in the region of interest. The goal is to produce a virtual area in more detail with lower turnaround time than the state of the art in geospecific region modeling. We demonstrate the application of our framework for creating detailed models of bridge structures using data typically available from GIS datasets.
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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 | 0.000 |
| 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 | 0.001 | 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