Blackheart in a Tender Apple Cultivar is Not Influenced by Using a Hardy Frame
Bibliographic record
Abstract
To determine the effects of rootstock and frameworking on hardiness, `Gravenstein' apple, which is not winter hardy, was grafted on trees frameworked with the hardy genotypes `Budagovsky 9' (B. 9), `Lobo', Kentville Stock Clone (KSC 28), or `Dudley', all of which were propagated on either `Beautiful Arcade' (BA) seedlings or on `Alnarp 2' (A. 2) rootstocks. For comparison, `Dudley' was grafted on `Dudley' frames propagated on both rootstocks. Growth after 8 years was greatest at Kentville; `Gravenstein' was larger than `Dudley', although when grafted, it was 40% smaller on the dwarf B. 9 than on the `Lobo' frame. On one night in Feb. 1993, all sites recorded temperatures below –30 °C. Blackheart was therefore measured in the rootstock trunk, framebuilder, and scion to document the resistance to this sublethal winter injury. Trees at the two colder sites, Truro and Centreville, had more blackheart than did those at the milder site. The percentage of blackheart in the trunk and frame was greatest for B. 9 and least for KSC 28. The tender scion, `Gravenstein', exhibited extensive blackheart regardless of site, rootstock, or the hardiness of the frame. The hardy scion, `Dudley', had some blackheart in the colder locations but none at Kentville. Blackheart levels in `Gravenstein' were very high on the framebuilder B. 9, and while generally less with the other hardy framebuilders, they were still high. While the hardy frames may have helped improve the survival of this cultivar, they did not change its hardiness status relative to `Dudley', even when `Dudley' was one of the hardy framebuilders.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".