Mauritia vinifera Mart Substrates and Nitrogen Doses in Acacia mangium Willd Growth
Bibliographic record
Abstract
The objective of this study was to evaluate the effect of the combination of a substrate formulated with decomposed buriti stem (Mauritia vinifera Mart.) and nitrogen doses in the production of Acácia mangium Willd seedlings. A completely randomized design was used in a 3 × 5 factorial scheme, being the factors constituted of substrates formulated from decomposed buriti stem (DBS) mixed with dystrophic yellow oxisol in three proportions (0; 25 and 50%) and nitrogen doses (0; 100; 200; 300 and 400 mg dm-3) applied in cover. After 60 days from the emergency, it was made the following evaluations: Plant Height (PH), stem diameter (SD), internodes number (IN), dry mass of the shoot part (DMA), length and dry mass of the roots, robustness quotient through stem height/diameter relation, roots/dry mass relation and Dickson Quality Index (DQI). The multivariate analysis of variance showed a significant difference (p < 0.01) among averages vectors of treatments. The grouping analysis for the evaluated treatments allowed the division into four distinct groups. In the bi-dimensional plane formed by the first two canonical variables (Can.1 and Can.2) that withheld 99,47% of the total variance contained in the nine original variables, it is observed that the group II of treatments, composed of substrates formulated with 50% of soil + 50% of DBS supplemented with the doses of 200, 300 and 400 mg dm-3 of nitrogen, has provided a greater initial growth of A. mangium plants, as stated by the highest averages in all evaluated variables.
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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 | 0.001 |
| 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".