Bibliographic record
Abstract
Three reliable methods are explained for estimating different types of cold hardiness in Malus. They include: 1) a whole plant controlled freezing experiment for the assessment of low mid-winter injury, 2) electrical impedance spectroscopy (Z), for the estimation of multiple freeze-thaw cycling injury and 3) a controlled freezing protocol to facilitate the rapid screening of large populations of Malus seedlings. The aim of this manuscript is not the results of these three methods but rather the description of the methods for cold hardiness testing. With the first method, plant mortality and morbidity (shoot, trunk and root regrowth) proved to be good indicators for evaluating low mid-winter cold hardiness. Of these, incremental root growth was the most sensitive to cold temperatures. The results from this study were validated by the good correlations between the laboratory findings and the 2004 field survival data from New York, USA following a test winter. The next method, Z, used root pieces of Ottawa 3 subjected to one, two and three controlled freeze-thaw cycles at temperatures of -3, -6, -9 and -12°C. Root tissue integrity, as measured by Z, was severely reduced with multiple events of freeze-thaw cycling and confirms that freeze-thaw cycling is more detrimental to apple rootstock viability than periods of constant freezing. Screening for cold hardiness in seedlings of Malus, 16–20 weeks after radicle emergence, was the third method and holds promise in segregating large... [to full text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.007 |
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".