Growth and Quality of Genotype PS-1319 Cacao Seedlings Produced Under Different Irrigation Depths
Bibliographic record
Abstract
Due to the lack of information on the water demand of cocoa seedlings, empirical techniques have been used in the supply of water to the seedlings, potentializing losses in their growth and development. In this context, the present study aimed to determine the optimal irrigation depth for a good development of the genotype PS-1319 cocoa seedlings. The study took place at the Federal Institute of Espírito Santo-Campus Itapina, located in the Colatina, a city situated in the northwestern region of the state of Espírito Santo, Brazil, in an experimental greenhouse of the campus, between October 20 and December 15, 2017. The experiment was conducted in a completely randomized design (CRD) using 20 seedlings of the genotype PS-1319 cacao per treatment. The treatments consisted of daily applications of six irrigation depths, corresponding to 4, 6, 8, 10, 12 and 14 mm d-1, being evaluated their effects on the morphological parameters (leaf area; dry mass of the aerial part, dry mass of the root system and total dry mass; height of the aerial part and stem diameter) and the quality (Dickson quality index). The applied depths interfered, both in the development and the quality of the seedlings, with quadratic adjustments for the leaf area, dry mass of the aerial part and total dry mass, diameter and for the Dickson quality index. The best responses to the studied parameters were provided by the 8.33 mm d-1 depth, which is recommended as an ideal for production of genotype PS-1319 cacao seedlings.
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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) | 0.001 | 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 | 0.001 |
| 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".