Biochar of Bamboo Influencing the Availability of P From Different Phosphate Sources in Dystrophic Yellow Oxisol of Amazon
Bibliographic record
Abstract
The residual biomass of leaves and branches of fast returning species, such as bamboo, are the most promising for biochar production, and could be used as an excellent soil conditioner to recuperate degraded areas, stock carbon and recycling nutrients. The experiment was carried out on greenhouse condition as completely randomized design, under a factorial arrangement (3×3), been three pyrolysis condition (400 ºC, 500 ºC and 600 ºC), applied at 40 t ha-1 and three phosphate sources: Nature Phosphate (NP), Simple Superphosphate (SS) and Triple Superphosphate (TS) (100 kg ha-1 as P2O5), during one year with four crop rotation of cowpea and corn. The presence of biochar, independently of pyrolysis temperature, allowed value of shoot dry matter highest in the first two crops rotation and the P contents in the soil were increased up to third crop, however, it is showing statistic difference only in the soil after the two cowpea crops, in which the interaction showed similar behavior between the more soluble source (TS) and the less soluble source (NP). The concentrations of foliar P were similar between the two cowpea crops, thus in the corn crops the concentrations of foliar P were higher in the second crop and showed significance in the two crops. The presence of biochar modified the nature behavior of the phosphate sources, becoming similar P availability.
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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".