Mapping changing temperature patterns over a glacial moraine using oblique thermal imagery and lidar
Bibliographic record
Abstract
Due to access difficulties in active alpine moraine environments, it can be challenging to accurately map and quantify debris cover and ice-core extent. To aid in identifying the presence and extent of ice-cored moraine, a non-invasive method of mapping spatial and temporal moraine temperature patterns using a light detection and ranging (lidar) digital elevation model (DEM) and sequences of oblique thermal imagery was evaluated. A procedure of lidar DEM-based orthorectification of thermal images collected through time from different locations enabled maps of temperature change to be generated and thermal signatures plotted. Although no exposed ice was visible on the moraine slope studied, the presence of shallow ice core beneath the debris-covered surface was inferred in areas of cooler temperatures during daylight solar heating and rapid thermal decay after sunset. It is presumed that this apparent increased heat loss in some areas of the moraine is being used to drive internal melt processes. It is believed that such temporal thermal imaging at high repetition frequency will aid in remotely mapping the presence of buried ice and, with the combination of energy balance data and further field validation, could enable the estimation of debris cover depth.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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 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".