Reducing Nitrous Oxide Emissions with Enhanced Efficiency Nitrogen Fertilizers in Wheat Production in Manitoba, Canada
Bibliographic record
Abstract
This project summarizes data from six site-years for a field study in Manitoba, with the aim of quantifying and comparing the application time (fall, spring) and source (EEF, non-EEF) effects on cumulative N2O emissions and agronomic measurements of Canadian hard red spring wheat in southern Manitoba, Canada. Core ideas from the study include: 1) At five out of six sites which had an N source effect on N2O emissions, eNtrench and SuperU were the most optimal products which reduced cumulative N2O emissions than conventional urea by 54% and 43%, respectively. 2) Combining fertilizer N sources, N2O emissions from fall treatments were similar to spring at four of six sites. 3) Grain yield was generally not affected by fertilizer N sources; 4) Grain yields and protein from fall applications were generally similar or lower than spring. This data publication includes all observations (location, agronomic management, grain yield, cumulative N2O emissions, and N removed with grain for each treatment-site-year). The dataset contains 384 observations for each variable of grain yield, cumulative N2O emissions and N removed with grain, which were collected at six sites in Manitoba over crop years from 2015 to 2017.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".