Errors Analysis Of Heat Pulse Probe Methods: Experiments and Simulations
Bibliographic record
Abstract
The heat pulse probe (HPP) method can be used to measure specific heat capacity (c), thermal conductivity (λ), and water content of soil; however, many factors are believed to contribute to measurement errors in both λ and c from the HPP method. The objective of this study was to examine if contact resistance, heat‐driven flow, temperature‐dependent thermal properties, or epoxy fillings affect the estimated soil thermal properties and water content from the HPP method. Heat pulse experiments were conducted on oven‐dry sand, water‐saturated sand, sand saturated with agar‐stabilized water, and coarse snow (large grain size). The results showed that for the sand and coarse snow, the thermal contact resistance did not contribute to the overestimation of heat capacity. There was little difference in measured water contents between sand saturated with water and sand saturated with agar‐stabilized water, suggesting no obvious heat‐driven flow under saturated conditions. Compared with an 8‐s heat pulse with a power strength ( q′ ) >80 W m −1 , a long heat pulse of duration 60 s and q′ < 6 W m −1 reduced the overestimation of soil c and water content (θ w ) on oven‐dry sands. Both temperature‐dependent soil thermal properties and epoxy filling inside the heater needle contributed to the overestimation of c and θ w.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".