Divergent recurrent selection for seedling tiller number in Altai wildrye
Bibliographic record
Abstract
Seedling tiller number is a possible selection criterion to improve seedling establishment of Altai wildrye, Leymus angustus (Trin.) Pilger, an important grass for autumn grazing of beef cattle in semiarid environments. Forty‐two half‐sib families selected for high seedling tiller number (HTN) and eighteen half‐sib families selected for low seedling tiller number (LTN) by four cycles of divergent recurrent selection were compared with four controls, Altai wildrye cultivars Prairieland, Eejay and Pearl, and crested wheatgrass (Agropyron desertorum (Fisch. Ex Link) Schultes), cultivar Nordan, on dryland and irrigated sites at Swift Current, Saskatchewan, Canada. Seedling tiller count, seedling height, tiller weight and seedling dry‐matter yield (DMY) were determined on two plants per plot and DMY was determined for each plot for 2 years post‐establishment. HTN half‐sib families had more, lighter and shorter tillers than LTN half‐sib families. There was a negative correlation (r=–0·42, P < 0·01, n=60) between seedling DMY and tiller number. HTN half‐sib families had higher DMY in post‐establishment years at the dryland site only. Seedling tiller number in Altai wildrye may be related to DMY at sites at which resource availability delays seedling establishment, but selection for HTN will not increase seedling DMY owing to concomitant changes in carbon allocation.
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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".