Native grasses : improving the seedling vigor and seed production of blue grama (Bouteloua gracilis) and prairie junegrass (Koeleria macrantha) ecovars (TM)
Bibliographic record
Abstract
Interest continues to grow in the utilization of native grasses for conservation, reclamation, Conservation Reserve Programs (CRP), right-of-ways, and wildlife habitat across North America. However, difficulty in establishment and limited availability of adapted seed sources has constrained the use of native grasses. The objectives of this study were to assess the effects of seeding rate, phosphorous fertilizer, Penicillium bilaii and soil texture on the establishment of blue grama and prairie junegrass ecovars TM, to examine the morphological distinctness and uniformity of a Manitoba blue grama ecovarrM, and to determine the potential for protection of this ecovar TM under the Plant Breeders' Rights Act of Canada. Blue grama and prairie junegrass row densities increased when seeding rate was doubled in a controlled environment; however seedling establishment as a percentage of seed sown decreased. Neither species responded to in-furrow P fefiilizer, fungal inoculant treatment, or a liquid foliar application of N. Soil type was the most important treatment for increasing establishment success, with the sandy loam providing the highest establishment rates and largest plants for both blue grama and prairie junegrass. The potential for the Manitoba blue grama ecovar TM to qualify for protection under the PBR Act of Canada was assessed as good...
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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".