Effect of climate change and use of improved varieties on barley and canola yield in Manitoba
Bibliographic record
Abstract
An, H. and Carew, R. 2015. Effect of climate change and use of improved varieties on barley and canola yield in Manitoba. Can. J. Plant Sci. 95: 127-139. A stochastic production function was estimated to investigate the effect of fertilizer inputs, changes in weather conditions and the use of improved varieties on barley and canola yields and its variability in Manitoba. Adoption of improved barley varieties did not have a significant effect on yield, while the adoption of herbicide-tolerant hybrid canola varieties was positively correlated with yield. An increasingly warmer climate in Manitoba is expected to have a slightly negative effect on mean barley yield and yield variance. In contrast, a warmer climate is expected to have a negligible effect on mean canola yield, but a positive effect on yield variability. Our results showed that a projected 50% increase in growing degree days would lead to a decrease of less than 1% in barley and canola yields.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".