A method to assess cold acclimation and freezing tolerance in asparagus seedlings
Bibliographic record
Abstract
Landry, E. J. and Wolyn, D. J. 2012. A method to assess cold acclimation and freezing tolerance in asparagus seedlings. Can. J. Plant Sci. 92: 271-277. Assessment of winter-hardiness using field-grown asparagus is complicated by variable, yearly climatic conditions and the large crown growing below the soil surface. The development of a seedling assay in controlled environments would be beneficial to study the physiology of winter-hardiness and to facilitate the selection of superior genotypes in breeding programs. Two cultivars, Guelph Millennium (GM) and Jersey Giant (JG), with differing patterns of autumn fern senescence in the field, where GM senesces earlier than JG, were compared. Seedlings were analyzed for physiological parameters after cold acclimation (10°C day/5°C night) or cold acclimation followed by sub-freezing (3°C to -3°C) in controlled environment chambers. Cold acclimation induced greater chlorophyll loss in GM than JG, consistent with previous field observations. LT50, the temperature at which 50% mortality occurs, decreased to approximately -8°C for both cultivars after the initial cold acclimation treatment. Subsequent subfreezing acclimation increased the LT50 for JG to -5°C, decreased freezing tolerance, while that for GM did not change. Early senescence and high proline concentration as well as stable protein and reducing sugar concentrations were associated with the freezing tolerance observed in GM. Further studies are required to establish if the cultivar differences for freezing tolerance identified here are correlated with experiments from field-grown plants.
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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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".