Substrates and Protected Environments in the Formation of Mouriri elliptica Mart Seedlings
Bibliographic record
Abstract
The successful establishment of a forest restoration program depends of the seedling quality, and the choice of substrate and growing environment plays a significant role in the production of high-quality seedlings. A study was conducted to evaluate the production of croada seedlings (Mouriri elliptica Mart.) grown in thirteen substrate combinations and subjected to two production nurseries constructed with black shading screen (Sombrite®) and aluminized thermal-reflective screen (Aluminet®). The substrates were prepared from different proportions of bovine manure, soil, vermiculite, and sand. In each protected environment, the thirteen different substrate compositions were arranged in a completely randomized design with five replicates of the five seedlings each. Because there was no replication of the cultivation environments, the joint analysis was carried out, allowing the comparison of the environments in the factorial scheme 2 × 13 (two environments × thirteen substrates). Growth and quality of seedlings were measured at 188 days. Seedling production environment has no effect on the germination and emergence process of the seedlings, but the growth and quality of the seedlings can be improved when grown in the nursery with black shading screen. Substrates containing low proportion of bovine manure (from 10 to 30%) and high proportion of vermiculite (from 30 to 40%) resulted in high-quality croada 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".