Greenhouse Gas Production and Transport Within Tile Drained Agriculture Systems
Bibliographic record
Abstract
Agriculture systems are becoming a growing concern regarding greenhouse gas (GHG) emissions; specifically, methane (CH4), carbon dioxide (CO2) and nitrous oxide (N2O).This study focused on GHG transport within two controlled tile drained agriculture sites in Eastern Ontario.Subsurface and surface greenhouse gas fluxes were monitored throughout a transect at each site with sampling locations in the farm field and the shoulder and slope of the riparian zone in the fall of 2017 and the 2018 agronomic season.All sampling locations showed similar levels of CO2 and N2O emissions; however, CH4 is observed as effluxes in oxidizing soils and influxes in reducing soils.GHG transport increases as soil depth decreases with maximum fluxes occurring at the soil/atmosphere interface.GHG transport is elevated in soil horizons that display larger concentration gradients and lower water saturation levels.Surface emissions are primarily influenced by GHGs produced and transported in shallow soil horizons.throughout the research and writing process.Dr. David Lapen for his insight on field practices and encouragement to find my niche within the larger Agriculture Greenhouse Gap Program (AGGP) project.Dr. Ian Clark for taking the time to discuss my findings and help with data interpretations.Emilia Craiovan and Mark Sunohara for their ongoing support with field practices.They are the backbone behind all AGGP field operations and none of this would have been possible without their support.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".