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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 teacher head, 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".