Improved characterization of frozen soil processes in the Versatile Soil Moisture Budget model
Bibliographic record
Abstract
Mohammed, G. A., Hayashi, M., Farrow, C. R. and Takano, Y. 2013. Improved characterization of frozen soil processes in the Versatile Soil Moisture Budget model. Can. J. Soil Sci. 93: 511–531. Soil freezing and thawing influence the infiltration of rain and snow melt water and subsequent redistribution, runoff generation, and a host of other processes. Accurate characterization of frozen soil processes in hydrological models is important for their use in managing agricultural activities and water resources. The Versatile Soil Moisture Budget (VSMB) is a relatively simple soil water balance model, which has been widely used in Canada for several decades, but its application has primarily been for crop-growing seasons. We have modified the VSMB to include new algorithms for snow accumulation and melt, soil freezing and thawing, and snowmelt infiltration and runoff; and evaluated its performance using field data from a grassland site in Alberta. The new VSMB model simulates snow processes with reasonable accuracy and predicts the day of thawing within several days of observation. It also estimates the amount of runoff and its inter-annual variability reasonably well, although the model still has limitations in accurately predicting the vertical distribution of water content. Despite these limitations, the model will be useful for estimating the amount of snowmelt runoff that provides the critical water inputs to wetlands and dugouts, and for understanding the effects of landuse variability on these processes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".