Growth Response of Four Conilon Coffee Varieties (Coffea canephora Pierre ex A. Froehner) to Different Shading Levels
Bibliographic record
Abstract
In order to select a conilon coffee (Coffea canephora Pierre ex A. Froehner) adapted to shade, four varieties (C153, JM2, LB1 and GG) were submitted to four shade levels (0, 30, 50 and 70) with evaluation of plant height, stem diameter, collar diameter, number of plagiotropic branches, number of fruits, root colonization by arbuscular mycorrhizal fungi and leaf anatomy. The experiment was carried out on a completely randomized design, in a 4 × 4 factorial scheme with ten replicates. We used organic compost based on cocoa shells for plant nutrition and cow urine for phytopathogenic fungi control. There was interaction between variety and shade factors for most of coffee characteristics analyzed. In general all coffee characteristics evaluated mainly fruit number and length of fruiting branches, significantly increased with increasing shade. Coffee varieties tested respond differently to the increasing of shade levels and leaf anatomy demonstrated the reduction of mesophyll thickness as the shading increased. The effect of shade levels on fruit yield fit to a positive linear regression for all four coffee varieties tested but the mycorrhizal colonization no presented differences among coffee varieties and shade levels. The varieties C153 and GG presented highlighted anatomical, growth and productive characteristics and can be indicated for shading cultivation, for example on agroforestry systems.
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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) | 0.001 | 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".