Fall defoliation affects acquisition of freezing tolerance and spring regrowth in asparagus
Bibliographic record
Abstract
Asparagus (Asparagus officinalis L.) acquires freezing tolerance during a period of fall acclimation when both photoperiod and temperature decrease. The above-ground vegetative growth may be important for sensing changing environmental conditions and translocating compounds to the below-ground crown. Defoliation experiments, repeated over 2 yr, were conducted by removing fern in mid-August, -September, and -October and evaluating crown metabolites and LT50, the temperature at which 50% of plants die, at monthly intervals to mid-November. Spring emergence and vigor were also assessed in separate experiments. In the first year, only mid-August defoliation affected LT50 values, decreasing freezing tolerance, which was associated with diminished rhizome proline concentration and storage root low- and high-molecular-weight fructan concentrations. All defoliation treatments in the second year decreased LT50 values, or increased freezing tolerance, possibly resulting from an interaction between defoliation and drought which increased rhizome sucrose concentrations. Defoliation decreased spring vigor in both experiments; the response was proportional to the earliness of the treatment and associated with rhizome and storage root fructan levels. Crowns of plants defoliated in mid-August had increasing proline concentrations during the fall, similar to control plants, suggesting the below-ground organs may have sensed soil temperature to cold acclimate. Autumn defoliation to control disease, harvest seed, or implement other cultural practices can reduce vigor and likely attenuate long-term performance of a plantation.
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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 | 0.000 |
| 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".