Best Practice for Measuring Permafrost Temperature in Boreholes Based on the Experience in the Swiss Alps
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Bibliographic record
Abstract
Temperature measurements in boreholes are the most common method allowing the quantitative and direct observation of permafrost evolution in the context of climate change. Existing boreholes and monitoring networks often emerged in a scientific context targeting different objectives and with different setups. A standardized, well-planned and robust instrumentation of boreholes for long-term operation is crucial to deliver comparable, high-quality data for scientific analyses and assessments. However, only a limited number of guidelines are available, particularly for mountain regions. In this paper, we discuss challenges and devise best practice recommendations for permafrost temperature measurements at single sites as well as in a network, based on two decades of experience gained in the framework of the Swiss Permafrost Monitoring Network PERMOS. These recommendations apply to permafrost observations in mountain regions, although many aspects also apply to polar lowlands. The main recommendations are (1) to thoroughly consider criteria for site selection based on the objective of the measurements as well as on preliminary studies and available data, (2) to define the sampling strategy during planification, (3) to engage experienced drilling teams who can cope with inhomogeneous and potentially unstable subsurface material, (4) to select standardized and robust instrumentation with high accuracy temperature sensors and excellent long-term stability when calibrated at 0°C, ideally with double sensors at key depths for validation and substitution of questionable data, (5) to apply standardized maintenance procedures allowing maximum comparability and minimum data processing, (6) to implement regular data control procedures, and (7) to ensure remote data access allowing for rapid trouble shooting and timely reporting. Data gaps can be avoided by timely planning of replacement boreholes. Recommendations for standardized procedures regarding data quality documentation, processing and final publication will follow later.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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 it