UAS-Deployed programmable Tri-Frequency GPR Data Gathering Along the Alaskan Steese Highway 01/24: Providing Geospatial Resources for Extreme Cold Weather Infrastructure Analysis and Subsurface Visualization
Bibliographic record
Abstract
GPR sensor data were collected on previous test sites that were tested during the summer field study exercise on Alaska's Steese Highway, as part of continued efforts to provide more geospatial data in Arctic regions relevant to cold region research. The Steese Highway is a major highway connecting the City of Fairbanks, Alaska, to the small town of Circle, Alaska, next to the Yukon River. The Steese Highway spans approximately 261 kilometers and is the only means of transportation for goods and supplies to the remote towns of both Central and Circle Alaska. The survey was conducted in January 2024 as a companion comparative dataset to the summer 2023 GPR dataset. The drone-flown GPR utilized three frequencies: 300 MHz, 150 MHz, and 100 MHz, to penetrate the ground in an effort to find the depth of permafrost in order to provide geospatial resources for extreme cold weather infrastructure analysis and subsurface visualization.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".