N<sub>2</sub> fixation by soybeans grown with different levels of mineral nitrogen, and the fertilizer replacement value for a following crop
Bibliographic record
Abstract
A field experiment was established to study the impact of added mineral N on the prediction of N2 fixation by soybean, and the consequences for the nature of any N credit that might be used to modify fertilizer recommendations to a following non-fixing crop. Nodulating and non-nodulating isolin es of soybean were grown with five rates of N fertilizer, and in a second year corn was grown in the same plots and its yield compared with a response curve. Yield, total N content, amount of N derived from soil, and fertilizer utilization of the nodulating isoline of soybean were not affected by fertilizer N. In contrast, mineral N inhibited nodulation and led to a decrease in the amount of N fixed. The balance of N in the soil was more negative for lower levels of applied N, but by the following spring the amount of mineral N in the soil was the same in all plots. The yield of corn was greater in the plots that had grown nodulating soybean than the non-nodulating isoline. The N fertilizer replacement value of 25 (8 kg N ha-1 resulted from a greater amount of root residues in the nodulating soybean, together with a C:N ratio that would favour faster mineralization than in the non-nodulating isoline. Key words: Corn, mineral N, N credit, fixation, soybean
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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".