Thermal remote sensing of ice-debris landforms using ASTER
Bibliographic record
Abstract
Abstract. Remote sensors face challenges in characterizing mountain permafrost and ground thermal conditions or mapping rock glaciers and debris-covered glaciers. We explore the potentials of thermal imaging and in particular thermal inertia mapping in mountain cryospheric research, focusing on the relationships between ground surface temperatures and the presence of ice-debris landforms on one side and land surface temperature (LST) and apparent thermal inertia (ATI) on the other. In our case study we utilize ASTER daytime and nighttime imagery and in-situ measurements of near-surface ground temperature (NSGT) in the Mediterranean Andes during a snow-free and dry observation period in late summer. Spatial patterns of LST and NSGT were mostly consistent with each other both at daytime and at nighttime. Daytime LST over ice-debris landforms was decreased and ATI consequently increased compared to other debris surfaces under otherwise equal conditions, but NSGT showed contradictory results, which underlines the complexity and possible scale dependence of ATI in heterogeneous substrates with the presence of a thermal mismatch and a heat sink at depth. While our results demonstrate the utility of thermal imaging and ATI mapping in a mountain cryospheric context, further research is needed for a better interpretation of ATI patterns in complex thermophysical conditions
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".