Agronomic Efficiency and Productivity of Sorghum in Response to Fertilizers With Different Phosphorus Solubilities in Greenhouse Conditions
Bibliographic record
Abstract
The experiment was conducted under controlled conditions in the municipality of Sobral, Ceará, Brazil, to evaluate the agronomic efficiency of phosphate fertilizers with different solubilities on the cultivation of sorghum in a Luvisol. The experimental design comprised randomized blocks in a 2 × 6 factorial scheme (two genotypes and five phosphorus treatments plus the control, without phosphorus). The treatments were: monoammonium phosphate, triple superphosphate, sheep manure, organic compost based on livestock residues, and organomineral fertilizer. Three blocks were used, and each plot comprised composed of a pot containing two plants. Sixty-five days after germination, the plants were harvested for evaluation of biometric data, biomass, accumulation of nutrients in the aerial part, soil fertility, agronomic efficiency index of the phosphate fertilizers and triple superphosphate equivalent. The organomineral and triple superphosphate fertilizers had the highest agronomic efficiency for production of sorghum biomass. The more soluble sources (organomineral, triple superphosphate and monoammonium phosphate) enabled a better phosphorus recovery rate and physiological efficiency than the sources with low solubility.
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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".