Non‐contact infrared temperature measurements in dry permafrost boreholes
Bibliographic record
Abstract
While planning the COAST Expedition to the Siberian Laptev Sea in 2005, the question of how to make a short equilibrium temperature measurement in a dry borehole arose. As a result, an infrared borehole tool was developed and used in three dry boreholes (up to 60.2 m deep) in the coastal transition zone from terrestrial to sub‐sea permafrost near Mamontovy Klyk in the western Laptev Sea. A depth versus temperature profile was acquired with equilibration times of 50 × 10−3 s at each depth interval. Comparison with a common resistor string revealed an offset due to limitations of accuracy of the infrared technique and the influence of the probe's massive steel housing. Therefore it was necessary to calibrate the infrared sensor with a high precision temperature logger in each borehole. The results of the temperature measurements show a highly dynamic transition zone with temperature gradients up to −0.092°C/m and heat flow of −218 mW/m. A period of submergence of only 600 years the drilled sub‐sea permafrost is approaching the overlying seawater temperature at −1.61°C with a temperature gradient of 0.021°C/m and heat flow of 49 mW/m. Further offshore, 11 km from the coastline, a temperature gradient of 0.006°C/m and heat flow of 14 mW/m occur. Thus the sub‐sea permafrost in the Mamontovy Klyk region has reached a critical temperature for the presence of interstitial ice. The aim of this article is to give a brief feasibility study of infrared downhole temperature measurements and to present experiences and results of its successful application.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".