Long-term simulation of snow cover and potential impacts on seasonal soil frost dynamics over croplands across Canada
Bibliographic record
Abstract
Abstract. Changes in weather patterns due to climate change pose a long-term threat to the ability of soil to retain nutrients or induce trace gas emission. Accurate simulation of overwintering conditions for farmland is crucial for predicting nutrient loss and crop growth under climate change. Snow cover is the common upper boundary condition that impacts the soil freeze-thaw dynamics, and it has been hypothesized that the reduced snow cover due to climate change may increase soil frost depth or duration. Nonetheless, such impact remains poorly understood and is rarely examined for farmland in the long-term experiment due to data scarcity. This paper aims to investigate the overwinter snow cover, soil temperature, and soil frost dynamics with the RZ-SHAW (Root Zone Water Quality Model with integrated SHAW model) in six research farms across Canada over a series of long-term simulations range from 1989 to 2020. The RZ- SHAW scenarios are calibrated and validated against the observed snow depth and soil temperature data. The same calibrated scenarios are then applied to simulate the soil frost depth and duration for each farmland to discover the potential influence of global warming on the soil frost dynamic.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".