Modeling the irrigation requirements for an experimental site in Northern Alberta, Canada
Bibliographic record
Abstract
A combined methodology of the Root Zone Water Quality Model (RZWQM), the generation of stochastic rainfall realizations, and an historical meteorological record were used to determine the supplementary irrigation requirement for an experimental site located in northern Alberta. The site receives an annual rainfall of approximately 500 mm yr -1 , and contains a fluctuating water table. The simulated results showed maximum irrigation requirements of 270 mm, however, half that amount can be required during an average or wet growing season of mean rainfall of 350 and 500 mm, respectively. The irrigation requirements were influenced by rainfall amount and distribution, downward flux and the subsequent fluctuation of the water table and the depth of water table at the beginning of the growing season, which was influenced by the winter season precipitation. The simulated results suggested that a water table less than 2 m deep from the ground surface can significantly reduce the irrigation requirements. Therefore, the winter precipitation and initial depth of the water table are suitable indicators of the likely requirement of irrigation during the growing season.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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) | 0.002 | 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".