A parsimonious water budget model for Canadian agricultural conditions
Bibliographic record
Abstract
This study used data collected from three cropland sites (two in Manitoba and one in Prince Edward Island) in Canada. In efforts to accurately describe the water dynamics in agricultural soils, most of the agri-hydrological models developed are highly complex, such that they require detailed input data, of which most of them are not always measured or otherwise readily available. However, the comprehensive and complex representations of the processes may not be justified when data is scarce. Thus, this study developed a soil water budget model that could describe the movement of water through Canadian agricultural soils using a few parameters only. The model was developed by selecting algorithms from various existing models, whose data requirements are readily available in literature and public Canadian databases. The model developed in this study can simulate evapotranspiration, runoff, deep percolation, and soil moisture content for various seasons (i.e., growing, non-growing/dormant, and pre-planting/post-harvest) with minimum data requirement, which addresses the limited availability of data in the country for crop and hydrological modeling. Moreover, the model is simple and easy to use, and can work without calibration. It was tested using three site-specific datasets, and results show that despite its simplicity, its overall performance was comparable with those of other agricultural models that use cascade flow framework for hydrology.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".