Nitrogen- and sulfur-deposition-altered soil microbial community functions and enzyme activities in a boreal mixedwood forest in western Canada
Bibliographic record
Abstract
Chronic nitrogen (N) and (or) sulfur (S) deposition to boreal forests in the Athabasca oil sands region (AOSR) in Alberta, Canada, has been caused by oil sands mining and extraction/upgrading activities. It is important that we understand the response of microbial community function to chronic N and S deposition as microbial populations mediate soil carbon (C) and N cycles and affect ecosystem resilience. To evaluate the impact of N and (or) S deposition on soil microbial community functions, we conducted a simulated N and S deposition experiment in a boreal mixedwood forest with the following four treatments: control (CK), N addition (+N, 30 kg N·ha −1 as NH 4 NO 3 ), S addition (+S, 30 kg S·ha −1 as NaSO 4 ), and N plus S addition (+NS, 30 kg N·ha −1 + 30 kg S·ha −1 ), from 2006 to 2010. Nitrogen and (or) S deposition did not change soil organic carbon, total N, dissolved organic C and N, or soil microbial biomass C and N. Soil microbial community-level physiological profiles, however, were strongly affected by 5 years of N and (or) S addition. Soil β-glucosidase activity in the +NS treatment was greater than that in the +S treatment, and S addition decreased soil arylsulfatase; however, urease and dehydrogenase activities were not affected by the simulated N and (or) S deposition. Our data suggested that N and (or) S deposition strongly affected soil microbial community functions and enzymatic activities without changing soil microbial biomass in the studied boreal forest.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".