Shoot type demography and dry matter partitioning: a morphometric approach in apple (<i>Malus</i>×<i>domestica</i>)
Bibliographic record
Abstract
In a study of the apple (Malus ×domestica Borkh.) canopy structure, 5-year-old 'Fuji' and 'Braeburn' trees grafted on a low-vigour rootstock (M9) were compared at both fruiting branch and shoot levels. Percentages of short ([Formula: see text]5 cm) shoots and short shoot leaf area were significantly higher on 'Braeburn' than on 'Fuji', (76.8% vs. 72.6% and 46.9% vs. 42.9% for 'Braeburn' and 'Fuji', respectively). This high percentage of short shoots as compared with literature data was probably due to the training method, which reduced vigour. At shoot level, the ratio between dry masses of axis and leaf, called the axialization index, was determined to compare short and long shoots. Axialization values were higher for 'Braeburn' than for 'Fuji'. Although overall and individual leaf area was greater on long shoots, long shoot axialization (0.64 and 0.54 for 'Braeburn' and 'Fuji', respectively) was approximately twice that of short shoots (0.36 and 0.24, respectively). Therefore, for short shoots, the reduced carbon investment in supporting tissues may explain the significant role short shoots played in supporting early fruit development. For long shoots, the longer time required to reach the autotrophic and then exporting stage as well as the detrimental effect of early extension shoot development on fruit set might be explained by greater axialization.Key words: long shoot, short shoot, axialization index, apple, Malus ×domestica, biomass partitioning.
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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 | 0.001 | 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 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".