Growth and Development of Bananas (Musa sp.) Cultivated in Agroecological Systems in Northeastern Brazil
Bibliographic record
Abstract
The banana is the most eaten tropical fruit and the second most harvested in the world, losing only to the orange. In this research, we aimed to evaluate the growth and development of different banana cultivars in response to the use of different types of seedlings and management methods used for plant propagation in agro-ecological systems. An experiment under randomized block design was applied in a factorial scheme of 2 × 2 × 2, with four blocks and two replicates in each block. The treatments comprised all combinations of the following sources of variation: cultivar—‘Pacovan’ and ‘Prata-anã’; seedlings weight—between 0.5 to 1.0 kg and above 1.0 kg; and method of propagation by rhizomes—with and without the acclimatization technique called “ceva”. The efficiency of each treatment was measured as the number of days to occur the following events: first sprouting, flowering, final harvest and the interval between flowering and harvest. No source of variation affected the day of flowering. Therefore, we fixed the value of flowering days as 260, independently of the cultivar or method of propagation. The analysis of the coordinate factors revealed that the variables that best explained the studied events were period of final harvest (92%), followed by interval between flowering and harvest (75%) and period of initial sprouting (71%). The propagation of ‘Prata-anã’ without “ceva” had the greatest efficiency, where as the propagation of ‘Pacovan’ without “ceva” had the worse efficiency. The propagations with “ceva” obtained intermediate values.
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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 | 0.000 |
| 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".