Alternative Substrate and Recipients for the Production of Arabica Coffee Seedlings
Bibliographic record
Abstract
The present work aimed to assess the quality of arabica coffee seedlings produced on different substrates and in various recipients. The work was performed in a randomized block experimental design, using three repetitions, in subdivided parcels, with three parcels, and four sub parcels. The experiment used the cultivar of Coffea arábia “Catuai IAC 44”. The genotype received the following treatments: R1 polyethylene bag, 615 cm³; R2: 280 cm³ tubes; R3: 120 cm³ tubes; S1 conventional substrate composed by a mixture of ravine earth with bovine manure at a 3:1 (v/v) proportion added with a NPK fertilization, recommended for coffee culture; S2 organic leguminous compound, mixture of a leguminous plant (guandu beans, Cajanus cajan) with bovine manure at a 1:1 (v/v) proportion, followed by a 90 days maturation process; S3: organic grass compound, produced by the composting of garden grass chips mixed with bovine manure at 1:1 proportion; and S4: vermicompost derived from the organic decompositions of grasses. The results of this research highlight that substrates and recipients affect the development of the seedlings of arabica coffee, improving the quality indexes of Dickson in the higher volume 615 cm3 recipient and the alternative substrate composed by legume, grass, and vermicompost were efficient to produce Arabica coffee seedlings. This combination of substrate and recipient can substitute the conventional substrate in this phase of the development of the seedlings, providing an increase in the production quality.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".