Investigating Microbial Triggers of Nitrous Oxide Emissions in Agriculturally Influenced Aquatic Ecosystems
Bibliographic record
Abstract
The agricultural sector in Canada is responsible for approximately 9% of greenhouse gas (GHG) emissions in Canada, accounting for 54 MT of carbon dioxide (CO 2 ), as well as 31% of methane (CH 4 ) and 75% of national nitrous oxide (N 2 O) emissions in 2021 (Environment and Climate Change Canada 2021). However, these estimates do not include the indirect GHG emissions that occur in agriculturally impacted waterways. Ontario is home to over 45 000 kilometers of agricultural drainage ditches, with tile drains directly connecting terrestrial and aquatic environments. Microbial biogeochemical cycles in the causeways experience fluxes of nutrients leading to hotspots for GHG at the sediment-water interfaces. Along with fluxes of nutrients, the causeways are regularly disturbed by anthropogenic effects (e.g., dredging, removal of vegetative buffers) and increasing frequency of storm events. Previous studies have used static chambers to evaluate GHG emissions from aquatic systems (Mu et al. 2022, Xiao et al. 2016). However, this approach can be time consuming and labour intensive and is impractical in aquatic systems due potential extensive underestimation of fluxes from diffusion. To resolve driving factors contributing to GHG in these systems a detailed study investigating the activity of the microbial community is warranted. In this study we hypothesize microbial activity within the sediment will correlate with N 2 O emissions. To test the response of the microbial community a combination of molecular approaches (i.e., qPCR and Ion torrent) targeting archaeal and bacterial nitrifiers and denitrifies was used. These functional responses were evaluated with respect to N 2 O emissions, which were measured in the field at the time of sampling using Unisense N 2 O probes. In this study, N 2 O sensor response was calibrated to a functional gene index for rapid risk assessment of GHG hotspots.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 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 | 0.001 |
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 teacher head, 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".