Evaluation of the Potential of Sewage Sludge for Manufacturing Substrate for Passion Fruit Seedlings
Bibliographic record
Abstract
Brazil is the world’s largest producer and largest consumer of passion fruit, producing approximately 0.1 million tons. However, crop management techniques are deficient in the use of alternative sources of fertilizer, an extremely relevant aspect in reducing production costs, as some nutrients are imported at high costs. Thus, this study was intended to calculate the percentage of an optimal dose of sewage sludge according to the regression model for each morpho-agronomic trait of yellow passion fruit. A completely randomized design (CRD) was adopted, consisting of four treatments, 0; 25; 50; and 75%, with 20 replicates considering one plant per replicate. Treatments were T1 (0 without sewage sludge addition), T2 (75% soil + 25% sewage sludge); T3 (50% soil + 50% sewage sludge); and T4 (25% soil + 75% sewage sludge). Regression coefficients were above 80%. Morpho-agronomic traits obtained optimal doses at a concentration of 50% of sewage sludge for the manufacture of the substrate. The conclusion reached was the substrate based on sewage sludge in the proportion of 50% combined with 50% of soil was superior to the other ones for seedling production.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".