On the use of heated needle probes for measuring snow thermal conductivity
Bibliographic record
Abstract
Abstract Heated needle probes provide the most convenient method to measure snow thermal conductivity. Recent studies have suggested that this method underestimates snow thermal conductivity; however the reasons for this discrepancy have not been elucidated. We show that it originates from the fact that, while the theory behind the method assumes that the measurements reach a logarithmic regime, this regime is not reached within the standard measurement procedure. Using the needle probe without this logarithmic regime leads to thermal conductivity underestimations of tens of percents. Moreover, we show that the poor thermal contact between the probe and the snow due to insertion damages results in a further underestimation. Thus, we encourage the use of fixed needle probes, set up before the snow season and buried under snowfalls, rather than hand-inserted probes. Finally, we propose a method to correct the measurements performed with such fixed needle probes buried in snow. This correction is based on a lookup table, derived specifically for the Hukseflux TP02 needle probe model, frequently used in snow studies. Comparison between corrected measurements and independent estimations of snow thermal conductivity obtained with numerical simulations shows an overall improvement of the needle probe values after application of the correction.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".