Do <i>Azospirillum brasiliense</i> and <i>Chlorella</i> sp. microalgae benefit the production of <i>Hymenaea courbaril</i> L<i>.</i> seedlings?
Bibliographic record
Abstract
The use of Azospirillum brasiliense and Chlorella sp. has gained popularity in agriculture and forestry due to their benefits to plants. These two biological solutions have demonstrated potential to improve the performance and sustainability of crops and forest species, opening new perspectives for agriculture, and the recovery of degraded areas. Given the importance of establishing management strategies for seedling production and that information on forest essences is insufficient, the aim of this study was to evaluate the effect of intervals of Chlorella sp . microalgae application and inoculation with A. brasiliense doses on the growth and quality of Hymenaea courbaril L. seedlings. The experiment was conducted in a randomized block experimental design, and treatments were arranged in a 5 × 4 factorial scheme, with four replicates, testing five A. brasiliense doses: 0, 3, 6, 9, and 12 mL/plant and four intervals of Chlorella sp. microalgae application: single application and every 7, 14, and 21 days after transplanting, for 120 days. Growth and photosynthetic parameters were evaluated. Inoculation with 6 and 9 mL of Azospirillum brasiliense, regardless of Chlorella sp. microalgae application favored the growth of H. courbaril seedlings. Chlorella sp. microalgae application at intervals of 14 and 21 days favored the responses of seedlings inoculated with A. brasilienses. The association between Azospirillum brasiliense and Chlorella sp. promoted gains in seedling production and 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) | 0.000 | 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 | 0.000 |
| 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".