Optimizing the role of pulses in crop rotation: biological nitrogen fixation
Bibliographic record
Abstract
The Pulse Research Network is a national research network aimed at optimizing the \nbenefits of pulses in rotation. The network is funded through Agriculture Agrifood \nCanada (AAFC) through the Agriculture Bioproducts Initiative program (ABIP). A \ngroup of scientists in the Department of Soil Science at the University of \nSaskatchewan and Agriculture Agrifood Canada – Saskatoon are involved in the \n“Cropping Systems Module”. Research streams in this module are focused on \nmaximizing 1) the N‐benefits, 2) the environmental benefits (i.e., carbon benefits), \nand 3) identifying best management strategies to optimize the beneficial role of \npulses in crop rotations. While the role of biological nitrogen fixation (BNF) in \nsupplying N to the pulse is generally well understood, the benefit of the pulse to the \nsubsequent crops in rotation is less clear. A particular focus of the research is to \nquantify root N and rhizosphere N contributions to the soil N pools. The general \napproach taken in the research is to label plants with stable isotopes (15N and 13C) \nand quantify contributions to soil N and C fractions. A key component to \nmaximizing the role of pulses in rotations is to determine how often a pulse should \nbe included to achieve maximum benefit. As a part of this objective, a study was \ninitiated to determine if frequency of inclusion affected BNF in the pulse year of the \nrotation. Results from this study are reported here.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".