Improving Ground Heat Flux Estimation: Considering the Effect of Freeze/Thaw Process on the Seasonally Frozen Ground
Bibliographic record
Abstract
Abstract An inadequate understanding of the changes in water and heat of the soil is likely a potential reason for the nonclosure of the surface energy balance. During the freeze/thaw period, the large volumetric heat capacity of ice and the latent heat of soil ice phase change had a specific influence on the estimation of ground heat flux (G0). However, the effects of the freeze/thaw process on G0 estimation were not well quantified using field observations. The improvements of G0 estimation by adding the soil ice heat storage (Δsice) and the latent heat of soil ice phase change (ΔsLH) were evaluated during the freeze/thaw period at two sites (Madoi and Maqu) on the eastern Tibetan Plateau. During the freeze/thaw period, both Δsice and ΔsLH had important influences on the reasonable determination of G0. Adding ΔsLH increased G0 at the daytime peak by 34.4 W/m2 (Madoi) and 9.3 W/m2 (Maqu). During the completely frozen stage, the contributions of Δsice to G0 ice + LH (adding both Δsice and ΔsLH) were higher than those during other freeze/thaw stages at both sites, and ΔsLH also made a significant contribution to G0 ice + LH. Therefore, the daily freeze/thaw cycles cannot be ignored during the completely frozen stage in the estimation of G0. The G0 ice + LH approach improved the G0 estimation more than G0 ice (adding Δsice) approach during the freezing and thawing stages. The improvements of G0 estimations increased the energy closure status and reduced the daily average of imbalance.
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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 | 0.001 |
| 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 | 0.000 | 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 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".