Effect of Blossom Density and Crop Load on Growth, Fruit Quality, and Return Bloom in ‘Honeycrisp’ Apple
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
From 2003 to 2006, the blossom level and crop load of ‘Honeycrisp’ apple ( Malus × domestica Borkh.) trees on M.26 rootstocks were adjusted to improve fruit quality and return bloom. The treatments consisted of manually removing flower clusters to 50, 100, and 150 per tree, then at ≈50 d after full bloom, the crop load was adjusted to 3, 6, and 9 fruit/cm 2 trunk cross-sectional area (TCSA), respectively. All flower and crop load adjustment significantly increased TCSA and canopy volume compared with the control. Classic biennial bearing was observed on the untreated control trees and those thinned to 150 blossom clusters per tree and 9 fruit/cm 2 TCSA and was mitigated for trees with 50 and 100 blossom clusters followed by crop load adjustment to 3 and 6 fruit/cm 2 TCSA, respectively. Fruit color the “on” year was always lower on the control trees; no difference was found in the “off” year. The treatments increased fruit weight proportional to crop load except for the 2004 “off” year. This study illustrates that for trees with ≈1 m 3 canopy volume, the combined effects of blossom and crop load adjustment to 100 blossom clusters/tree followed by fruitlet adjustment to 6 fruit/cm 2 TCSA and below will induce consistent annual production for ‘Honeycrisp’.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 it