Divergent recurrent selection for seedling tiller number in Altai wildrye
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 it