Divergent selection for flowering in annual bluegrass and its impact on plant development and turfgrass utility
Bibliographic record
Abstract
Annual bluegrass (Poa annua L.) is a widely occurring turfgrass component. Recent efforts to develop cultivars of this species have led to increased interest in selection methods. The objectives of this study were to evaluate the utility of controlled growth studies for divergent selection for flowering and to evaluate the resultant selections for development and turfgrass quality. Two growth cabinet selection runs (1997 and 1998) were made at a constant 20°C and a 16-h photoperiod at low light levels. Selection was based upon the earliest and latest plants to produce a seedhead on the main stem. In the second selection run, mid-flowering selection was made to characterize the population distribution. Selections were grown out in the greenhouse in 1998 and 1999. Selections made in 1998 were seeded into a field study in August 1999 and maintained as golf green turf. Late selections flowered later than early or mid selections and produced more leaves on the main stem prior to inflorescence appearance. Seedhead ripening showed greater uniformity in late selections. Some late selections produced few seedheads under the greenhouse conditions. Selection for divergent flowering types was successful using the method employed. Turf performance was not related to flowering type, indicating that screening for turf performance will be required after initial selection for plant type. Key words: Poa annua L., selection, life history, tillering, seed production, turf performance
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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 | 0.001 | 0.001 |
| 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".