Analysis of ground penetrating radar data using hierarchical Markov Chain Monte Carlo simulation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Ground penetrating radar (GPR) is a geophysical method used in highway maintenance to determine subsurface conditions within the right-of-way. GPR operates by using short-pulse radiation of radio-frequency electromagnetic energy to record dissimilarities in electrical properties of subsurface materials. As such, GPR results are susceptible to the transmission frequency used and the inherent properties of different subsurface materials. Uncertainty due to these susceptibilities can lead to ambiguity in the interpretation of GPR data. To distinguish heterogeneity from uncertainty, this paper modeled GPR data on pavement layer thickness using Markov Chain Monte Carlo (MCMC) simulation. MCMC is able to model heterogeneity within a given dataset and was employed to estimate and predict layer thicknesses obtained from GPR data. Simulated results were consistent with field data and provided statistical estimates of missing values in the original dataset. This analysis will aid relevant stakeholders to verify and determine consistency in field GPR data.
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.
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 | 0.001 | 0.001 |
| 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