Phosphate Organomineral Fertilizer Usage Compared to Mineral Phosphate in Corn Cultivation
Bibliographic record
Abstract
Corn has great relevance for agribusiness as it is used in human and animal food, besides the energy matrix. The objective of this study was to assess the leaf content of macro and micronutrient, accumulation of primary macronutrients, production components and productivity with the application of pelleted organomineral fertilizer in different doses compared to mineral fertilization, in two soils of different textures in the corn crop. Two trials were conducted in the municipality of Uberlândia-MG-Brazil: one located near the highway BR 452 km 141, at the coordinate 18°55′26″S, 48°09′36″W, clay soil. The other at km 640 of BR 365, at coordinate 18°54′05″S, 48°25′20″W, sandy soil. A randomized block design with five replications was used, with five doses of organomineral fertilizer (40, 60, 80, 100 and 120% of the recommended dose of P2O5 for corn) and an additional with 100% of the recommendation of mineral P2O5, total of six treatments and 30 plots. The results showed that, with the exception of phosphorus leaf content, yield in the sandy texture and the number of grains per row in the clay texture, there was no difference between the doses of phosphate organomineral and did not differ from the mineral. Thus, phosphate organomineral is as efficient as mineral for phosphorus supply.
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".