Development of a Rooted Cutting Propagation Method for Selected Arbutus Unedo L. Types and Seasonal Variation in Rooting Capacity
Bibliographic record
Abstract
In this study, the rooting abilities of semi-hardwood cuttings from 8 Arbutus unedo L. (strawberry tree) types were evaluated. For this purpose, cuttings were taken at two different vegetation periods in July and November. The collected cuttings were treated with IBA (2, 4, 6, 8 and 10 g/l) and placed into a misting system in July and bottom-heated system in November in the greenhouse. The rooting rate, survival rate and root quality were determined during the study. The cutting collection period significantly affected rooting ability, and according to the results obtained the best rooting performance was obtained with July cuttings except for types 1 and 8. There were differences among the A. unedo L. types in rooting ability; Type 4 had the highest rooting rate (87.01% in July and 70.71% in November) among the types studied. The percentage of rooted cuttings ranged from 0% to 100% for the control and IBA treatment for both periods and cuttings taken in November required higher concentrations of IBA for rooting than cuttings taken in July. The survival rate of cuttings varied according to type in the acclimatization stage, the best types being type 3 and type 4. July cuttings of all types obtained shoots in the rooting media.
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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 | 0.001 | 0.000 |
| 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".