Comparing the performance of the DNDC, Holos, and VSMB models for predicting the water partitioning of various crops and sites across Canada
Bibliographic record
Abstract
Accurately accounting for water budgets within regional agroecosystems is becoming an increasingly important practice, as both climate change and water consumption pressures have the potential for influencing agro-productivity and other water use activities. In this study, water budget measurements from 10 rainfed experimental sites across Canada were utilized to evaluate the performance of three models for their water partitioning capabilities: denitrification–decomposition (DNDC), Holos, and versatile soil moisture budget (VSMB). To assess the likely model performance at an upscaled national level, the models were applied at the site level with no water component-specific calibration. Evapotranspiration (ET) was found to be the dominate component of the water budget at the prairie sites (89%–149% of precipitation) (i.e., in comparison to runoff, tile drainage, and deep percolation), while both ET (37%–73% of precipitation) and drainage (19%–61% of precipitation) represented most of the water outflow budget at the sites in eastern and Atlantic Canada. As DNDC integrates daily crop growth dynamics with nitrogen, water, and heat stresses, in contrast to VSMB and Holos, which only utilize a water budget model, it was not surprising to find that DNDC consistently out-performed the other two models across all the statistical performance metrics considered at daily resolution.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".