Growth and Quality of Inga heterophylla Willd Seedlings According to the Slow Release Fertilizer
Bibliographic record
Abstract
Slow release fertilizers have become an alternative for better efficiency in substrate fertilization for seedlings production, however, there are not many studies approaching the use of such fertilizers in the production of native forest species seedlings. This work aimed to evaluate the effect of different doses of a slow release fertilizer (SRF) on the development and quality of the Inga heterophylla seedlings. The experiment was conducted in a vivarium with 50% of shade for a 150-day period. Randomized blocks were the chosen experimental design, constituted of four treatments and four replications, with twenty plants per experimental unit. The treatments were four doses of Osmocote® FLL (0, 4.1, 8.2 and 12.3 g dm-3) in NPK 15-09-12 formulation, with evaluated variables being the height of the seedling (H), collar diameter (CD), number of leaf pairs (NLP), leaf area (LA), aerial part dry mass (APDM), root dry mass, total dry mass (TDM) and Dickson quality index (DQI). All the evaluated parameters responded significantly to the SRF doses and fit the positive quadratic polynomial model. For all of the analyzed variables, results show that the best averages were obtained by using the doses between 5.7 and 6.5 g dm-3, but due to the quadratic response they presented a decrease in the mean values after doses which were superior to the maximum performance point of each characteristic.
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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".