Development of a Tool to Predict Soil Moisture and Soil Temperature Regimes
Bibliographic record
Abstract
Physico‐biochemical processes occurring in soil are difficult to predict because the knowledge of local soil properties such as soil moisture and temperature is often limited. Therefore, soil moisture and temperature regime classes are necessary for US soil taxonomy and other classification systems. The goal of this study is to develop a modeling tool to predict soil moisture and temperature at multiple soil horizons and to code the Keys to Soil Taxonomy in a Soil Moisture and Temperature Regime Classification (SMTRC) module for automatic identification of soil moisture and temperature regimes. The Environmental Policy Integrated Climate (EPIC) model was extended as the EPIC–SMTRC tool for this purpose. Field data from the Soil Climate Analysis Network (SCAN) sites and the Wye farm site in Maryland for validation of soil moisture and temperature predictions. Results indicate that predicted daily soil temperatures are in close agreement with observed values with R 2 values ranging from 0.73 to 0.98 and Nash–Sutcliffe efficiency (NSE) from 0.50 to 0.96. Predicted soil moisture by EPIC–SMTRC captured observed trends reasonably well. Testing results at the Wye farm indicate that the predicted daily values of water content are satisfactory, with R 2 values ranges from 0.66 to 0.88 and NSE from 0.51 to 0.84. The EPIC–SMTRC tool demonstrated the ability to automate the identification of the soil moisture and temperature regimes as currently defined in the US soil taxonomy and can be used to classify soils and to be utilized for other sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".