Effect of plant age and cottony snow mold on winter survival of forage grasses
Bibliographic record
Abstract
The low temperature basidiomycete (LTB, syn. Copribus psychromorbidus Traquair), the causal agent of cottony snow mold, is a major constraint to forage grass survival and productivity in the parkland region of the Canadian prairies under prolonged snow cover (e.g., 160 d). Studies were conducted to establish the level of the snow mold resistance in seven grass species commonly grown in western Canada and to identify the seeding dates that permit grass plants to develop maximum levels of resistance to snow mold. Following attack by the LTB fungus, considerable variation in winter survival and forage yield was observed among the grass species. Smooth brome and meadow brome were most resistant, followed by timothy and creeping red fescue. Tall fescue and orchardgrass were the most susceptible. Controlled-environment and field studies demonstrated that orchardgrass seeded in late spring resulted in greater winter survival and dry matter yield than when seeded in July or August, both in snow mold inoculated and noninoculated treatments. Additional mortality and dry matter yield loss were linked to snow mold injury. These results demonstrated that snow mold injury could reduce winter survival and yield in first-year forage grasses, especially in orchardgrass, and early seeding could reduce the impact of winter stresses. Key words: Grass, orchardgrass, timothy, smooth bromegrass, meadow bromegrass, creeping red fescue
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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 | 0.000 |
| 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".