Evaluation of Adaptability, and Vegetative and Generative Traits of Some Peach Cultivars under Meshkinshahr Environmental Condition
Bibliographic record
Abstract
Peach [Prunus persica (L.) Batsch] is one of the important stone fruits cultivated in subtropical and temperate zones. In temperate zones, peach faces many problems such as late spring cold, incompatibility, low quality, quantity and low yield. In order to select the best peach cultivars in Meshkinshahr, 25 cultivars of peach were planted in Simple Lattice design (SLD) with two replications and 25 experimental blocks with four trees in each block and 3×4 m spacing. In 3 years’ research (from 2008- 2010) vegetative traits such as trunk diameter, annual growth, tree height and canopy extension and repoductive traits (beginning and ending of flowering, flowering period, harvest date, fruit growth period, yield, TSS, acidity, flesh and skin color) were evaluated. After combined analysis of variance, most cultivars showed proper morphological and pomological compatibility in the studied zone. SunCrest, Dixired, Robin, SpringCrest, Earligold, Amesdn, Alberta, Earlired, Red top, Baby gold 7 and Paeez e Meshkin cultivars showed the best compatibility of vegetative traits. J. H. Hale, Red skin, Loring, Red top, Dixired, Baby gold 7, SunCrest and Meril sundans had the highest yield. SpringCrest, Earliglo, Dixired and Earlired were recommended as early cultivars, Alberta, Loring, Redtop, Baby gold 7 and SunCrest as midseason cultivars, and J. H. Hale, Red skin, Meril sundans and Paeez e Meshkin as late season cultivars. Therefore, the latter cultivars are recommended for planting new orchards.
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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".