Proportion of Leaves and Floral Tillers and Nutritional Quality of Prolific and Non-Prolific Seed Yielding Cultivars of Smooth Bromegrass
Bibliographic record
Abstract
In the semi-arid clima\e of the Canadian Prairies most herbage production occurs in May and June whereas the grazing season lasts until October. Summer and fall pasture consists largely of mature, quiescent vegetation with senescent leaves and lignified stems. Floral tillers of some temperate grasses have a higher rate of leaf senescence than non-floral tillers (Bittman et al., 1988) and are rejected by grazing cattle. Floral tillers of Stipa viridula Trin. and Pascopyrum sinithii (Rydb.) Love had 2 and 4 % units lower protein content and digestibility, respectively, than non-floral tillers (White, 1983). In the Parkland region of the prairies, smooth bromegrass (Bromus inermis Leyss.) is the most commonly seeded pasture grass. Cultivars of smooth bromegrass have been selected for high seed yielding potential to ensure their commercial success. The smooth bromegrass cultivars that produce seed prolifically in Canada (Carlton, Magna, and Signal) are derived from the northern ecotype while the less prolific varieties are derived from the southern ecotype. In the seed growing areas of the Canadian Prairies, southern cultivars yielded about 47 % less seed than northern cultivars (Knowles, 1984). The objective of this study was to compare the proportion of floral tillers and leaves and the nutritional quality of northern (prolific) and southern (non-prolific) cultivars of smooth bromegrass.
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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 | 0.001 | 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) | 0.000 | 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".