Seasonal Vegetative Growth in Genotypes of Coffea canephora, as Related to Climatic Factors
Bibliographic record
Abstract
Knowledge about the seasonality of different genotypes of Coffea canephora is an important tool for this crop management, particularly with regard to irrigation and fertilisation issues. This study was conducted in Espírito Santo, Brazil and aimed at to evaluate the seasonal vegetative growth in genotypes of C. canephora, as related to climatic factors, based on the growth of groups of orthotropic and plagiotropic branches with different ages. Three groups of plagiotropic branches and one group of orthotropic branches of 14 genotypes (Ipiranga and 13 that belonged to the variety Vitória) were selected and marked to followed along the one-year experiment. Three-year-old plants were cultivated under full-sun conditions, with a spacing of 3 m between rows and 1 m between plants. The growth rates of the orthotropic and plagiotropic branches differed among the genotypes and underwent seasonal variation during the entire year, with high correlations to the air temperature. Under the natural experimental conditions, the growth rate of the branches decreased when the minimum air temperatures were below 17.2ºC for most of the genotypes studied. The plagiotropic branches presented lower vegetative growth, mainly for the coffee berries, compared to the younger branches. Presumably, the genotypes of C. canephora demanded more nutrients for growth between mid-September and the second week of May.
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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) | 0.000 | 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".