Refinement of the nocturnal boundary layer budget method for quantifying agricultural greenhouse gas emissions
Bibliographic record
Abstract
Measuring greenhouse gas (GHG) emissions directly at the farm scale is most relevant to the agricultural sector and has the potential to eliminate some of the uncertainty arising from scaling up from plot or field studies or down from regional or national levels. The stable nighttime atmosphere acts as a chamber within which sequentially-measured GHG concentration profiles determine the flux of GHGs. With the overall goal of refining the nocturnal boundary layer (NBL) budget method to obtain reliable flux estimates at a scale representative of the typical eastern Canadian farm (approximately 1 km2), fluxes of CO2, N2O, and CH4 were measured at two agricultural farms in Eastern Canada. Field sites in 1998 and 2002 were located on an experimental farm adjacent to a suburb southwest of the city of Ottawa, ON, a relatively flat area with corn, hay, and soy as the dominant crops. The field site in 2003 was located in the rural community of Coteau-du-Lac, QC, about 20 km southwest of the island of Montreal, a fairly flat area bordered by the St. Lawrence River to the south, consisting mainly of corn and hay with a mixture of soy and vegetable crops. A good agreement was obtained between the overall mean NBL budget-measured CO2 flux at both sites, near-in-time windy night eddy covariance data and previously published results. The mean NBL-measured N2O flux from all wind directions and farming management was of the same order of magnitude as, but slightly higher than, previously published baseline N2O emissions from agroecosystems. Methane fluxes results were judged to be invalid as they were extremely sensitive to wind direction change. Spatial sampling of CO2, N2O, and CH4 around the two sites confirmed that [CH4] distribution was particularly sensitive to the nature of the emission source, field conditions, and wind direction. Optimal NBL conditions for measuring GHG fluxes, present approximately 60% of the t
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".