Réponse du soya à la fertilisation minérale en éléments majeurs NPK sur des sols classés de riches à excessivement riches en phosphore ou en potassium des Basses Terres du Saint-Laurent
Bibliographic record
Abstract
In the scientific literature there are numerous studies on soybean response to major mineral NPK elements. But there are few research data on this topic for the Lowland soils of the Saint-Lawrence Valley. A 3-yr study was conducted in 1994, 1995, and 1996 on three representative soil types of the lowland Saint-Lawrence Valley region: Dujour, Sainte-Rosalie and Saint-Urbain. These soils are classified from rich to excessively rich in available phosphorus and potassium as measured by the Mehlich 3 method. Results of this study indicate that yield is rarely significantly influenced by levels of nitrogen, phosphorus or potassium fertilization. Moreover, there are no significant interactions among those elements and soybean grain yield. Other observed variables (specific weight, grain visual quality, 100-seed weight, seed protein and oil contents) were generally not affected by the different levels of nitrogen, phosphorus or potassium fertilization. There were few significant interactions between major elements and those variables. Generally, soybeans did not respond to NPK mineral fertilization on representative soils of the Lowland Saint Lawrence Valley region classified from rich to excessively rich in available phosphorus and potassium. Key words: Soybean, fertilization, nitrogen, phosphorus, potassium, yield
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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".