Substrates and Irrigation Frequencies in the Development of Seedlings of Schizolobium parahyba var. amazonicum
Bibliographic record
Abstract
Knowledge on the ideal conditions for the formation of high quality seedlings is fundamental to guarantee establishment success of crops in a safe and efficient manner. Here, we evaluate the effect of different substrates and irrigation frequencies on the initial growth of parica (Schizolobium parahyba var. amazonicum) seedlings. The experiment was conducted in a greenhouse at the Federal Rural University of Amazônia, Capitão Poço, PA. Several variables were analyzed including seedling height, stem diameter, number of leaflets, shoot dry matter, root dry matter, total dry matter, height and stem diameter ratio, shoot dry matter ratio and root dry matter. We found significant differences in seedling development between the applied treatments, including a significant interaction between substrate type and irrigation regime on seedling height, stem diameter, the number of leaflets and plant growth indices, with the best response for proportions 75% soil + 25% bovine manure and 50% soil + 50% bovine manure. Therefore, the substrates containing organic compounds resulted in a higher quality of the seedlings, while the sand consistently presented the lowest increases in seedling production under the three experimental irrigation frequencies, and thus is not recommended as a substrate for the development of Schizolobium parahyba var. amazonicum.
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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".