Spring thaw and growing season N<sub>2</sub>O emissions from a field planted with edible peas and a cover crop
Bibliographic record
Abstract
Nitrous oxide emissions are highly episodic and to accurately quantify them annually, continuous measurements are required. A tower-based micrometeorological measuring system was used on a commercial cattle farm near Cô teau-du-Lac, (QC, Canada) during 2003 and 2004 to quantify N2O emissions associated with the production of edible peas. It was equipped with an ultrasonic anemometer and a fast-response closed-path tunable diode laser. Continuous measurements of N2O fluxes were made during the spring thaw following corn cultivation in summer 2002, then during an edible pea growing season, followed by cattle manure application, cover crop planting and through until after the next spring ploughing. The cumulative N2O emissions of 0.7 kg N2O-N ha-1 during the initial snowmelt period following corn harvest were lower than expected. Sustained and small N2O emissions totalling 1.7 kg N2O-N ha-1 were observed during the growing season of the pea crop. Solid cattle manure applied after the pea harvest generated the largest N2O emissions (1.9 kg N2O-N ha-1 over 10 d) observed during the entire sampling period. N2O emissions associated with the cover crop in the fall were mostly influenced by manure application and totalled 0.8 kg N2O-N ha-1. For the subsequent spring thaw period, N2O emissions were 0.8 kg N2O-N ha-1. This represents approximately 15% of the annual emissions for the edible pea-cover crop system, which totalled 5.6 kg N2O-N ha-1 over the measuring periods. There was little difference in spring thaw N2O emissions between the two growing seasons of corn and edible pea-cover crop. Key words: Nitrous oxide emissions, legumes, snowmelt, dairy manure, tunable diode laser, flux tower
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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 | 0.001 | 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".