Induction of Lateral Branching in Sweet Cherry (Prunus avium L. cvs. “Siah Mashhad” & “Dovomras”) Trees in Nursery
Bibliographic record
Abstract
This study was carried out in two independent experiments on one year old sweet cherry (Prunus avium L. cvs. “Siah Mashhad” and “Dovomras”) nursery trees with the main purpose of improving lateral shoot formation and increasing the quality of trees. In the first experiment, heading treatments (0, 40, 60 and 80 cm above ground) and in the second experiment, Arbolin treatments (0, 5, 15, 25 mL·L-1) was investigated. Trees were treated with foliar sprays of Arbolin in 2 times at 7-days intervals in mid-June. At the end of the growing season, the tree quality was measured on the basis of their diameter and height of trees, number, length and angels of lateral shoots. A factorial experiment was laid out in a completely randomized block design with 3 replications where each plot contained 10 trees. Results showed that all of the treatments increased the number of laterals in comparison with the control. The cultivars had different response to the treatments. Heading in 60 cm was the best treatment for improving total number of lateral shoots. The result of second experiment showed that there were significant differences between cultivars and Arbolin treatments. The number of lateral shoots enhanced with application of higher concentrations and repeated Arbolin treatments in both cultivars. Arbolin treatments had more significant effect on the number and length of lateral shoots than heading treatments.
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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 | 0.001 |
| 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".