Development and Leaf Morphofunctional Attributes of Native Species Used in Oil Well Base Revegetation
Bibliographic record
Abstract
This study aimed to evaluate the influence of soil preparation and mineral fertilization on according to the morphological growth and leaf morphofunctional attributes of native species used in the revegetation of the well base area of oil extraction. The experimental design was in randomized blocks in a split-plot scheme with 4 replications. The plots were the two soil types, and the subplots were the 5 planting fertilization treatments with NPK 04-14-08 with 0, 40, 80, 160 and 320 g pit-1. Four native species were planted and individually evaluated. The species responded in a variable way depending on the applied fertilization. The type of soil statistically influenced the number of leaves of Inga laurina, showing a greater number when cultivated in clayey soil. The other species did not differ in terms of soil type. For planting fertilization, it is recommended to apply 219.27 to 227.25 g pit-1 for Schinus terebinthifolius Raddi and 189.83 g pit-1 for Mouriri guianensis. The application of planting fertilizer for S. terebinthifolius Raddi and M. guianensis is recommended. The species Inga laurina, Garcinia brasiliensis and Chrysobalanus icaco developed better without planting fertilization. Leaf attributes demonstrated an adaptive response of plants regarding to environmental stress conditions to which they were submitted.
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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 | 0.000 |
| 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".