Characterization of Populations of Turf‐Type Perennial Ryegrass Recurrently Selected for Superior Freezing Tolerance
Bibliographic record
Abstract
ABSTRACT Perennial ryegrass (Lolium perenne L.) is an important turfgrass species used for lawns, sports fields, and recreational areas. Insufficient tolerance to subfreezing temperatures compromises its persistence in northern climates. A recurrent selection method, entirely performed indoors, was applied to two initial genetic backgrounds to generate populations putatively more tolerant to freezing (TF populations). The objective of the present study was to assess physiological and molecular responses after four cycles of selection (TF1–TF4). Freezing tolerance and cold‐induced metabolites were monitored in plants hardened to natural variations in temperatures in fall and winter in an unheated greenhouse. Recurrent selection improved freezing tolerance expressed as the lethal temperature for 50% of the plants (LT50) and the vigor of regrowth after freezing. Significant changes in the levels of total and individual cold‐induced carbohydrates (fructans) and amino acids (glutamine and proline) in crowns of hardened plants occurred in response to selection. Both groups of metabolites showed an opposite response to selection. The observation of DNA polymorphisms and progressive genetic differentiation between the initial populations and advanced cycles of selection suggests an impact of selection on allelic composition. Recurrent selection had a positive impact on freezing tolerance of perennial ryegrass through modifications in the molecular and genetic makeup of the populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".