Nitrogen Economy of Pulse Crop Production in the Northern Great Plains
Bibliographic record
Abstract
Previously published data were used to examine the N economy of pulse crops typically grown on the Northern Great Plains with the goal of assessing the potential contribution of field pea ( Pisum sativum L.), lentil ( Lens culinari s Medik.), chickpea ( Cicer arietinum L.), common bean ( Phaseolus vulgaris L.), and faba bean ( Vicia faba L.) to soil N accretion. Incremental changes in soil N associated with the pulse crops (i.e., the nitrogen increment, N inc ), were strongly correlated to N 2 fixation and were highly variable. Data suggest that crops that can achieve relatively high levels of N 2 fixation, such as faba bean, field pea, and lentil are more likely to contribute positively to the overall N economy, particularly when a cropping system is evaluated over a long term. In contrast, pulse crops that typically achieve only modest levels of N 2 fixation such as desi and kabuli chickpea and common bean are more likely to be either N neutral or contribute to a soil N deficit. Because of extreme variability in levels of N 2 fixation achieved, presumably reflecting variability in soil productivity as well as variations in local climate and weather, the N inc of pulse crops likewise is highly variable. Thus, the N contribution to a subsequent crop is difficult to predict with any certainty, particularly on a yearly or short‐term basis.
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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 | 0.001 |
| 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".