Landform mapping, elevation modelling, and thaw subsidence estimation for permafrost terrain using a consumer-grade remotely-piloted aircraft
Why this work is in the frame
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Bibliographic record
Abstract
We assess performance of a small consumer-grade remotely-piloted aircraft (RPA) for landform mapping, elevation modelling, and thaw subsidence estimation in continuous permafrost terrain. We acquired RPA imagery near Rankin Inlet, Nunavut, to construct orthomosaics and digital elevation models (DEMs) that we use to interpret geomorphology and surficial geology. We estimate seasonal thaw subsidence using DEM differences. To quantify accuracy, RPA DEMs are compared with a satellite-based reference elevation. Subsidence estimates are compared with measurements from differential interferometric synthetic aperture radar (DInSAR). We find that RPA images are very effective for mapping periglacial landforms and surficial geology with the chosen flight specifications. The DEMs exhibit vertical mean absolute error of approximately 1 cm at ground control points. Away from control points, relative vertical accuracy is approximately 3 cm. Comparison to the reference elevation results in survey-wide vertical mean absolute errors of 33–66 cm with high variability and spatial autocorrelation of elevation discrepancy. There is local agreement between DEM differences, DInSAR, and on-the-ground measurements of seasonal subsidence. Results suggest that small RPA may be applicable for mapping thaw subsidence on the order of a few centimetres near control points. However, DEM differences are influenced by vegetation and are contaminated by spatially-variable artefacts, preventing reliable survey-wide RPA estimation of seasonal thaw subsidence.
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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 | 0.001 | 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