Spatially tripartite interactions of denitrifiers in arctic ecosystems: activities, functional groups and soil resources
Bibliographic record
Abstract
Soil denitrification is one of the most significant contributors to global nitrous oxide (N(2) O) emissions, and spatial patterns of denitrifying communities and their functions may reveal the factors that drive denitrification potential and functional consortia. Although denitrifier spatial patterns have been studied extensively in most soil ecosystems, little is known about these processes in arctic soils. This study aimed to unravel the spatial relationships among denitrifier abundance, denitrification potential and soil resources in 279 soil samples collected from three Canadian arctic ecosystems encompassing 7° in latitude and 27° in longitude. The abundance of nirS (10(6) -10(8) copies g(-1) dry soil), nirK (10(3) -10(7) copies g(-1) dry soil) and nosZ (10(6) -10(7) copies g(-1) dry soil) genes in these soils is in the similar range as non-arctic soil ecosystems. Potential denitrification in Organic Cryosols (1034 ng N(2) O-N g(-1) soil) was 5-11 times higher than Static/Turbic Cryosols and the overall denitrification potential in Cryosols was also comparable to other ecosystems. We found denitrifier functional groups and potential denitrification were highly spatially dependent within a scale of 5 m. Functional groups and soil resources were significantly (P < 0.01) correlated to potential denitrifier activities and the correlations were stronger in Organic Cryosols. Soil moisture, organic carbon and nitrogen content were the predominant controls with nirK abundance also linked to potential denitrification. This study suggests that the dominant control on arctic ecosystem-level denitrification potential is moisture and organic carbon. Further, microbial abundance controls on ecosystem level activity while undoubtedly present, are masked in the nutrient-poor arctic environment by soil resource control on denitrifier ecosystem level activity.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".