Identification of regulatory genes to reduce N<sub>2</sub>O production
Bibliographic record
Abstract
Siciliano, S. D. 2014. Identification of regulatory genes to reduce N2O production. Can. J. Plant Sci. 94: 1033–1036. The production of nitrous oxide occurs predominantly by microbial activity. This microbial activity can be broadly sub-divided into denitrification, the sequential reduction of nitrate to nitrous oxide or dinitrogen gas, or into nitrification, the sequential oxidation of ammonia to nitrite. The consumption of nitrous oxide occurs by microbial activity as well, but only by a single pathway, i.e., the activity of nitrous oxide reductase (nos). The purpose of this investigation was to determine the dominant producer of nitrous oxide in our agricultural ecosystems, and then explore how these producers interacted with other biological and edaphic factors to regulate overall nitrous oxide production. Finally, we also investigated what controlled nitrous oxide consumption in these agricultural ecosystems. Much to our surprise, the dominant production of nitrous oxide in these upland agricultural soils occurred by nitrification, likely the nitrification-denitrification pathway. In addition, a root exudate, formate, was a large driver of nitrous oxide release via its interaction with the fungal biomass under micro-aerophilic conditions. Despite these unusual sources of production, what became apparent was that the net flux of nitrous oxide in an agricultural soil was linked to denitrifier consumption of nitrous oxide. In conclusion, this project found that there was a wide variety of non-bacterial denitrifier producers of nitrous oxide in an agricultural soil and that they interact not only between themselves but with the plant community. However, the net production of nitrous oxide in agricultural fields was still tightly linked to bacterial denitrification, but through the consumption of nitrous oxide by bacterial denitrifiers.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".