Supplemental Material: Drone-Based Thermal Infrared Imagery for Detection of Cold Groundwater Exfiltration in Shár Shaw Tagà, Yukon, Canada
Bibliographic record
Abstract
This study investigates the role of a rock glacier interacting with the Shár Shaw Tagà River (Grizzly Creek) riverbed in the St. Elias Mountains (Yukon, Canada), using a unique multimethod approach that integrates hydro-physicochemical and isotopic characterization, drone-based thermal infrared (TIR) imagery, and visible time-lapse (TL) imagery. The final phase of the study focused on characterizing the extent and magnitude of groundwater-surface interactions underlined by the spring inventory and hydrochemical analysis. Due to the large spatial scale (several hundred meters) and the challenges associated with differential stream gauging in proglacial environments, stream temperature heterogeneity was selected as a proxy for detecting groundwater exfiltration. To this end, a drone-based thermal infrared (TIR) survey was conducted in June 2024 to identify zones of groundwater exfiltration. This survey provided a detailed map of preferential groundwater exfiltration locations, contributing to the overall understanding of the rock glacier’s influence on the riverbed hydrologic system. Drone-based TIR video surveys were conducted on 28 June 2024, between 8:00 and 10:00, to maximize the coverage of shaded sections of the stream. The surveys were conducted using a DJI Mavic Enterprise 3T, equipped with a DJI RTK module and a DJI D-RTK 2 mobile station for GNSS base-station support. The Mavic 3T features a 48-megapixel RGB camera with a 24 mm focal length and a 640x512-pixel thermal camera with a 40 mm focal length. The drone was manually controlled to optimize the capture of surface temperatures across wide sections of the Shár Shaw Tagà River, recording both TIR and RGB videos simultaneously. Flight altitudes ranged from 5 to 20 m, depending on the section. The survey began 180 m upstream of the N1 subsection and ended 800 m downstream of the N2 subsection. Due to difficulties in flying over the “narrow section”, it was surveyed twice at different elevations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.025 | 0.004 |
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".