Biochar of Sawdust Origin in Passion Fruit Seedling Production
Bibliographic record
Abstract
Great part of solid waste are deposited inappropriately near cities or in rural areas, contributing to negative environmental impacts. There are numerous forms of waste processing, one of them is pyrolysis to produce biochar and subsequent use in agricultural systems. The objective of this study was to evaluate different substrates with activated biochar (AB) and biochar (B) in substrate and its effect on the growth of passion fruit seedlings. The test was conducted in a nursery, located in Sinop/MT, May to July 2013, designed in four blocks with ten treatments: commercial substrate (CS), composed by pine bark and vermiculite 4:1; nursery substrate (NS) composed by carbonized rice husk and coconut fiber 1:1; and the eigth treatments represented were B and AB additions of 25; 50; 75; and 100% in CS, (B25, B50, B75, B100, AB25, AB50, AB75 and AB100, respectively). After 60 days sowing the stem diameter, plant height, leaf number was evaluated and at the end of the experiment the fresh and dry weight of shoots and roots, and Dickson Quality Index (DQI) were assessed. AB at concentrations of 25, 50 and 75% combined with commercial substrate showed increases in parameters fresh and dry biomass weight, height, stem diameter and number of leaves. The dose of 25% AB is presented as the best dose to be adopted in commercial crops. The sawdust processed into AB is an alternative in the production of passion fruit system and the reintegration of this raw material to the productive sector.
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 | 0.000 |
| 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".