Collect Seasons, Indolebutyric Acid and Substrates on Rooting of ‘Bengal’ Lychee Cuttings
Bibliographic record
Abstract
Lychee (Litchi chinensis Sonn.) propagation by cuttings is a promising alternative in commercial production of seedlings, since it is an easy procedure, in addition to enabling the standardization in the field and the reduction in production costs. Therefore the objective of this work was to evaluate ‘Bengal’ lychee cuttings rooting, collected in spring and summer, using different concentrations of indolebutyric acid (IBA) and substrates. The experimental design was completely randomized, in a 3 × 3 factorial arrangement, with 5 replications and 10 cuttings per plot, and the following factors were adopted: IBA concentrations (0; 1,000 and 2,000 mg L-1) and types of substrates (carbonized rice husk, vermiculite medium granules and coconut fiber). At 140 days, the following variables were evaluated: cuttings survival; leaf maintenance; cuttings unrooted with calli; cuttings rooted; roots number per cutting; root length and root dry mass. In addition, the pH, electrical conductivity, density and water retention capacity of the substrates were evaluated. There was no difference in IBA concentrations applied in both evaluated periods. However, in the spring, the carbonized rice husk provided highest mean for the measured variables. On the other hand, in the summer, the rooting percentage presented a lower average in all substrates (< 1%), independent of the IBA concentrations. It was concluded that the ‘Bengal’ lychee propagation by cutting can be carried out in the spring, using carbonized rice husk as substrate, without IBA.
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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) | 0.001 | 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) | 0.001 | 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".