Bibliographic record
Abstract
This project involved using a UAV to create digital models and products that enhance visualization and understanding of the moraines of White and Thompson Glaciers, Axel Heiberg Island, Nunavut. UAV surveying offers an effective alternative to high-resolution satellite imagery for documenting highly dynamic proglacial landscapes. Proglacial landforms and sediments provide insights into glacier characteristics and the behavior of glacier termini. UAV surveying allows for high-resolution examination of moraine features and the creation of digital elevation models (DEMs) that facilitate topographic modeling and the detection of changes. A Mavic 3M Drone was employed with UGCS flight planning software. UGCS flight planning software was selected for its ability to create orthomosaic flight plans that consider ground elevation, ensuring accurate Ground Sampling Distance (GSD). A 2m Arctic DEM was uploaded to UGCS prior to planning flight routes, allowing for more precise measurements. Post processing was done in Drone2Map and Agisoft Metashape to create both 2D orthomosaics and 3D models respectively. The results of this work will be used as a visual tool to study current aspects of the moraines, including extent, depositional processes, and present hydrodynamic energy environments. This information will be compared to historical air photos and previous studies to examine changes the moraines over a sixty-year period.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".