A method to characterize root morphology traits in alfalfa
Bibliographic record
Abstract
Productivity in alfalfa (Medicago sativa L.) has been shown to be influenced by root morphology. Our objectives were to identify the optimum age, cultural practices, and environmental conditions to characterize taproot diameter (TD), lateral root number (LRN), fibrous root mass (FRM), and determinate taproot percentage (DTP) in alfalfa. No correlations were found between greenhouse-cultured plants and 21-wk-old field-grown plants for LRN or FRM, while TD showed low to moderate correlations between greenhouse and field environments. Fourfold more plants with determinate taproots were identified in transplanted plots compared to seeded plots. All root traits were affected by plant spacing but, no germplasm × plant spacing interactions were found. Solid seeded plants needed more time to show maximum expression of root traits and scored lower for LRN and FRM and had smaller TD than spaced plants. Only TD had a significant germplasm × location interaction. Both TD and LRN increased with N fertilizer and between the seeding and first production years, but no germplasm × N rate or germplasm × year interactions were found. Rankings of alfalfa germplasms were the same at the end of the seeding year (22 wk after planting) and at the end of the first production year (74 wk after planting). Characterization of LRN and FRM in alfalfa should be conducted in seeded field plots with uniform plant spacing at one location, with or without N fertilizer at least 22 wk after planting. A similar protocol with evaluation at more than one location would be more appropriate for characterization of TD. Key words: Medicago sativa L., root morphology, alfalfa
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".