Effect of leaf spotting diseases on grain yield and seed traits of wheat in southern Saskatchewan
Bibliographic record
Abstract
Although leaf spotting diseases have been reported to have a negative effect on grain yield and seed characteristics of wheat (Triticum spp.), the magnitude of such effects on wheat grown on dryland in southern Saskatchewan is not known. A fungicide experiment was conducted at Swift Current (Brown soil) and Indian Head (Black soil) from 1997 to 1999 to determine the effect of leaf spotting diseases on yield and seed traits of wheat. Two fungicides, Folicur 3.6F and Bravo 500, were applied at different growth stages on three common wheat (Triticum aestivum L.) and three durum wheat (T. turgidum L. var durum) genotypes. Fungicide treatments generally did not affect yield, kernel weight, test weight or grain protein concentration, and these effects were relatively consistent among genotypes. Folicur applied at head emergence in 1997 and at flag leaf emergence and/or head emergence in 1998 increased yield at Indian Head (P < 0.05). Fungicides applied at and before flag leaf emergence tended to increase kernel weight. Grain protein concentration increased only in treatments of Bravo applications at Indian Head in 1998. These results suggested that under the dryland environment and management in southern Saskatchewan leaf spotting diseases generally have a small effect on yield, kernel weight, test weight and protein concentration. Key words: Wheat, leaf spotting diseases, fungicide, yield
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".