Reactive Natural Phosphate in Safflower Fertilization in Cerrado Oxisol
Bibliographic record
Abstract
Fertilizer management has a direct influence on crop productivity, especially phosphorus, which is most limiting to the development of crops in tropical soils due to the genesis of these soils. In this sense, it is necessary to use nutrient sources that are agronomically efficient at reduced costs compared to conventional sources. Thus, the objective of the present study was to evaluate the effects of reactive natural phosphate as a source of phosphorus on the development, growth, and yield of safflower in Cerrado Oxisol. The experiment was carried out in a greenhouse at the Federal University of Mato Grosso, Campus of Rondonópolis. The completely randomized design consisted of the following treatments: 0, 100, 200, 400 and 600 mg dm-3 of reactive natural phosphate (Bayóvar reactive phosphate), with 6 replicates, consisting by pots with 2 dm3 of capacity. To the Oxisol used to fill the plots was incorporated dolomitic limestone to increase base saturation to60%. Safflower cultivar used was IMA 0213 with a final population of three plants per plot. Plant height, number of leaves and chlorophyll index were evaluated at 15, 30, 45 and 55 days after emergence. In the last evaluation plants were cut and the number and diameter of the chapters, shoot and chapters dry mass, volume and root dry mass were also evaluated. The results were submitted to analysis of variance and regression up to 10% probability. In general, safflower crop shows a positive response to application of reactive natural phosphate. Doses between 389 and 600 mg dm-3 promoted best results for development and safflower production in an Oxisol.
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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".