Quantitative effects of soil organic matter on thermal conductivity modeling
Bibliographic record
Abstract
• A large dataset consists of 1569 measurements from 208 soil samples was compiled. • Sensitivity of 15 models and 5 soil organic matter (SOM) levels were evaluated. • The normalized model is optimal for predicting thermal conductivity (λ). • λ is largely dependent on bulk density and water content. • The effect of SOM on λ modeling is minimal with SOM content ≤ 2.5 %. Soil thermal conductivity ( λ ) is a crucial parameter for energy transfer between the land and the atmosphere. An increasing number of studies have noted that changes in soil organic matter content ( ϕ som ) considerably influence λ . However, only a few studies have focused on quantifying the effects of soil organic matter (SOM) on λ modeling. In this study, the performances of 15 λ models that consider the effects of SOM were evaluated based on a compiled dataset consisting of 1569 measurements from 208 soils (0 %≤ ϕ som ≤40 %, mass content). Two random forest-based explainable artificial intelligence models, Shapley additive explanations (SHAP) and permutation importance (PI), were also applied to assess the SOM effects. The results showed that the model developed from normalizing Kersten function had the best prediction accuracy because of its realistic function types and parameter settings. The models of Johansen (1975), Balland and Arp (2005), Su et al. (2016), and Yan et al. (2019) performed the best, with NSE ≥ 0.71 and RMSE ≤ 0.3 W m -1 K -1 . Moreover, PI and SHAP demonstrate that the effect of SOM on λ is nonlinear, and the influence of SOM should be considered when ϕ som >2.5 %. There is a strong interaction between SOM and bulk density when ϕ som >2.5 %, significantly influencing λ .
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".