Influence of production systems on return and risk from malting barley production in western Canada
Bibliographic record
Abstract
Profitable malting barley (Hordeum vulgare L.) production systems are required to reverse the decline in area seeded to malting barley in western Canada. Systems that could increase the profitability of growing malting barley considered the previous crop, nitrogen (N) rate, and fungicide application. The net return (NR) and risk for these systems were computed from western Canada field data. Analysis of variance was used to evaluate the impact of systems on NR over 21 site-years of field data. Risk was evaluated with a stochastic simulation model. NR was higher and risk lower for malting barley when the preceding crop was field pea (Pisum sativum L.), when fungicide was applied at the flag leaf stage for leaf disease control, and when N was 50% of the recommended N rate. Therefore, malting barley should be grown on field pea stubble at a lower N rate, with fungicide applied when there are leaf diseases.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".