Phosphorus fertilizer by-product effect on the interaction of zinc and phosphorus in corn and soybean
Bibliographic record
Abstract
The application of high phosphorus fertilizer by-products to farmland is a common practice used to reduce storage volume and disposal cost. However, because Iowa soils are generally high in both phosphorus and zinc, there are environmental and fertility questions concerning their application to farmland. A greenhouse study was conducted to determine the effect of a soil-applied fertilizer by-product on the zinc-phosphorus relationship in corn and soybean plants grown on three Iowa soil series: Belinda, Downs, and Haig. The soils were mixed with a high-phosphorus fertilizer by-product and plants were also collected and analyzed for phosphorus and zinc content and uptake. Soil samples were analyzed for phosphorus and zinc content. The experiment was repeated, switching pots to assess the residual effect after one cycle of rotation. The treatments were arranged in a totally randomized design with three replications. The results indicated that the high phosphorus fertilizer by-product increased soil and plant concentrations of phosphorus in soybean and corn plants and soils. However, the increase of P concentration did not induce changes in the phosphorus-zinc interaction in corn and soybean plants in any of the studied soils. Key words: Phosphorus, corn, soybean, zinc, sludge, by-products, Zn-P relationship, zinc-phosphorus interaction
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".