Genetic Variation and Heritability Estimates for Indirect Measures of Freezing Tolerance during Fall Acclimation in Asparagus
Bibliographic record
Abstract
Winterhardiness is an important trait for asparagus ( Asparagus officinalis ) cultivars grown in temperate climates. Several biochemical and physiological parameters are correlated with the acquisition of freezing tolerance during cold acclimation in the fall and could be used as indirect measures for selection in a breeding program. Genetic variation was assessed before and after fall acclimation in August and November, respectively, for freezing tolerance attributes in 18 asparagus hybrids and 24 clones, which included male, supermale, and female genotypes. Fern chlorophyll and rhizome sucrose concentrations and storage root and rhizome percentage water decreased, whereas the concentrations of storage root proline, glucose and sucrose, and rhizome proline and high-molecular-weight fructan increased during the fall. Germplasm did not differ in August but significant variation was observed in November for most parameters, indicating genotype-specific responses to fall acclimation and the acquisition of traits associated with freezing tolerance. Narrow-sense heritability estimates were significant for fern chlorophyll, storage root proline, and rhizome glucose and sucrose concentrations. With significant genetic variation and heritability, breeding to improve freezing tolerance could be possible with indirect selection measures.
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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 | 0.001 |
| 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".