Application of distributed temperature sensing for mountain permafrost mapping
Bibliographic record
Abstract
Abstract Permafrost distribution in mountains is typically more heterogeneous relative to low‐relief environments due to greater variability in the factors controlling the ground thermal regime, such as topography, snow depth, and sediment grain size (e.g., coarse blocks). Measuring and understanding the geothermal variability in high mountains remains challenging due to logistical constraints. This study presents one of the first applications of distributed temperature sensing (DTS) in periglacial environments to measure ground surface temperatures in a mountain permafrost area at much higher spatial resolution than possible with conventional methods using discrete temperature sensors. DTS measures temperature along a fibre‐optic cable at high spatial resolution (i.e., ≤ 1 m). Its use can be limited by power supply and calibration requirements, although recent methodological developments have relaxed some of these restrictions. Spatially continuous DTS measurements at a studied rock glacier provided greater resolution of geothermal variability and facilitated the interpretation of bottom temperature of snowpack data to map patchy permafrost distribution. This research highlights the potential for DTS to be a useful tool for permafrost mapping, ground thermal regime interpretation, conceptual geothermal model development, and numerical model evaluation in areas of heterogeneous mountain permafrost.
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How this classification was reachedexpand
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 | 0.000 | 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 teacher head, 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".