Factors Affecting Establishment of Sweet Cherry on Gisela 6 Rootstock
Bibliographic record
Abstract
Cristalina and Skeena sweet cherry cultivars ( Prunus avium L.) on Gisela 6 ( Prunus cerasus × Prunus canescens ) rootstock were maintained for the first four growing seasons in a randomized, replicated split-split plot experimental design with two main plot irrigation frequency treatments, the two cultivars as subplots and three soil management subsubplot treatments. The same amount of irrigation water was applied through four drip emitters per tree at either high (I1, four times daily) or low frequency (I2, once every second day) beginning in the second year. Three different soil management treatments were established the year of planting and included: 1) NK fertigation with a herbicide strip (control), or additionally; 2) maintenance of a 10-cm thick bark mulch over the herbicide strip; and 3) annual fertigation of 20 g phosphorus (P) per tree per year immediately after bloom. I1 irrigation increased soil moisture (0- to 20-cm depth) throughout each growing season. The I1 irrigation resulted in higher leaf and fruit concentrations of the immobile nutrients P and potassium (K) and larger trunk cross-sectional area than I2 trees. I1 irrigation, in general, did not affect initial yield or fruit size. Fruit from I2 irrigation had higher soluble solids concentration (SSC), color, and total phenolic concentration at harvest in 2008 and lower titratable acidity (TA), firmness, and stem pull force suggesting an acceleration of fruit maturity. When compared with the control soil management treatment, P fertigation resulted in leaves and fruit with higher P concentrations, a higher 2008 crop yield, and a delay in 2008 crop maturity as indicated by lower harvest color and SSC and higher stem pull force. Mulch application, relative to control treatments, resulted in trees with higher vigor (but only with I1 irrigation) and leaf K concentration and had few effects on initial fruit yield or quality. There were important differences in cultivar responses to treatments. ‘Cristalina’ vigor was lower than ‘Skeena’ whose fruit had lower firmness and pedicel retention than ‘Cristalina’.
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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 | 0.001 |
| 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".