Characterization of meadow × smooth bromegrass hybrid populations using morphological characteristics
Bibliographic record
Abstract
Three hybrid populations between meadow bromegrass (Bromus riparius Rehm.) and smooth bromegrass (Bromus inermis Leyss.) have been developed using recurrent selection for vigor, uniformity of growth and floret fertility. The objective of this study was to use morphological characters to characterize the three hybrid populations (S-9197, S-9073, and S-9183) and the two parental species. Tiller heights did not differ among the hybrids and the parental species. Leaf-to-stem ratio of the hybrids was intermediate to that of the parents. Tiller density, panicle density and dry matter yield of the hybrids were more similar to those of smooth bromegrass. Leaf pubescence densities of the hybrids were higher than the parental species, but pubescence lengths closely resembled meadow bromegrass. Leaf area index (LAI) of the hybrids was lower than smooth bromegrass, and resembled meadow bromegrass. Lowest brown leaf spot ratings were observed in meadow bromegrass, while the hybrids were similar to smooth bromegrass. In general, the hybrid populations showed some individual characteristics similar to each of the parental species and, thus, can be characterized as being intermediate to the parental species. Furthermore, several of the differences among the hybrid populations can be attributed to the selection criteria that were used to develop these populations. Key words: Bromus, smooth bromegrass, meadow bromegrass, hybrid bromegrass, morphological characteristics
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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) | 0.001 | 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".