An evaluation of GPR monitoring methods on varying river ice conditions: A case study in Alaska
Bibliographic record
Abstract
Ice roads and bridges across rivers, estuaries, and lakes are common transportation routes during winter in regions of the circumpolar north. Ice thickness, hydraulic hazards, climate variability and associated warmer air temperatures have always raised safety concerns and uncertainty among those who travel floating ice road routes. One way to address safety concerns is to monitor ice conditions throughout the season. We tested ground penetrating radar (GPR) for its ability and accuracy in measuring floating ice thickness under three specific conditions: 1) presence of snow cover and overflow, 2) presence of snow cover, and 3) bare ice, all common to Interior Alaska rivers. In addition, frazil ice was evaluated for its ability to interfere with the GPR measurement of ice thickness. We collected manual ice measurements and GPR cross-sectional transects over 2 years on the Tanana River near Fairbanks, Alaska, and for 1 year on the Yukon River near Tanana, Alaska. Ground truth measurements were compared with ice thickness calculated from an average velocity model created using GPR data. The error was as low as 2.3–6.4% on the Yukon River (Condition 3) and 4.6–9.5% on the Tanana River (Conditions 1 and 2), with the highest errors caused by overflow conditions. We determined that certain environmental conditions such as snow cover and overflow change the validity of an average velocity model for ice thickness identification using GPR, while frazil ice accumulation does not have a detectable effect on the strength of radar reflection at the ice-water interface with the frequencies tested. Ground penetrating radar is a powerful tool for measuring river ice thickness, yet further research is needed to advance the ability of rural communities to monitor ice thickness using fewer time-intensive manual measurements to determine the safety of ice cover on transportation routes.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".