How to Improve the Production and Quality of Chirimoya (Annona cherimola Mill.) in the Tropical Andes
Why this work is in the frame
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Bibliographic record
Abstract
The production of chirimoya (Annona cherimola Mill) is seasonal; therefore, the fruit's prices and availability are compromised. This research aimed to develop technologies to improve the production and competitiveness of off-season chirimoya fruit production. This research was conducted in the subtropical valley of Tumbaco, Pichincha province, Ecuador. Chemical defoliants (zinc sulfate, hydrogenated cyanamide, and copper chelate) and a sprouting inducer (hydrogenated cyanamide) were evaluated to standardize and increase defoliation improve sprouting, and consequently bring the harvest season forward. The study was conducted on eight-year-old trees of San José de Minas and MAG-Tumbaco genotypes. The assessed variables were defoliation, sprouting, elapsed flowering time, and harvest period. The fruit harvest was shortened by 18.4 days by applying the defoliants and sprout inducer. For the San José de Minas genotype, the best response for defoliation (99%) was copper chelate at 1%, compared to the control with 58.9% defoliation at 35 days after its application. In the MAG-Tumbaco genotype, the best defoliation results were also obtained with cooper chelate at 1% (99.7% defoliation), while the control achieved 95.5% for 35 days after its application. In the latter genotype, defoliants significantly outperformed the control in terms of sprouting at 21 and 28 days after application; as a result, harvest was advanced by 22.6 days. These treatments should be tested in other climate zones to establish them as cultural practices to increase off-season fruit production, benefiting farmers and industry.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| 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 it