Growth and Productivity of Irrigated Coffee Trees (Coffea arabica) in Ceres-Goiás
Bibliographic record
Abstract
The objective of this work was to evaluate the growth and productivity of cultivars and progenies of arabica coffee under irrigation by driping in Ceres-Goiás. It was conducted in the experimental area of the Goiano Federal Institute-Ceres Campus. A total of 35 treatments were randomized blocks with four replications, from January 2017 to August 2018. At 30 and 36 months after planting, the diameter of the orthotropic branch, canopy diameter, plant height, number of nodes in the plagiotropic branch 1, length of the plagiotropic branch 1, number of nodes in the plagiotropic branch 2, length of the plagiotropic branch 2, length of the plagiotropic branches 1 and 2, number of nodes of the plagiotropic branches 1 and 2 and productivity were evaluated in 2018. The linear simple correlations were estimated in all evaluated characteristics. There was a difference in growth and yield of the evaluated genotypes. There is a positive correlation among the vegetative characters and the productivity. Catucaí Amarelo 2SL presented higher growth than the other evaluated genotypes. The genotypes Catiguá MG 1, Acauã Novo, Acauã 2 and 8, Catucaí Amarelo 24/137, Catucaí Amarelo 2SL, Asa Branca, Paraíso H419-10-6-2-10-1, Catuaí Vermelho IAC 15, Acauã, Sarchimor MG 8840, IPR 98, Araponga MG 1 and Obatã Vermelho IAC 1669-20 were the ones that had the highest productivity.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".