Complexity of nutrient enrichment on subarctic peatland soil CO <sub>2</sub> and CH <sub>4</sub> production under increasing wildfire and permafrost thaw
Bibliographic record
Abstract
Abstract. The adverse impacts of excessive soil nutrients on water quality and carbon sequestration have been recognized in tropical and temperate regions, with already widespread industrial farming and urbanization, but rarely in subarctic regions. However, recent studies have shown significant increases in porewater nitrogen (N) and phosphorus (P) concentrations in burned subarctic peatlands and downstream waters, which is a growing concern as climate change leads to increasing wildfires, permafrost thaws, and waterlogged peatlands. In this study, we present the results of a short-term incubation experiment conducted on soils from subarctic bogs and fens, aimed at evaluating the effects of high levels of nutrients on carbon gas production rates. We divided aliquots of the peatland soil samples into separate containers and added artificial porewater to each, enriching them with dissolved inorganic nitrogen (N), phosphorus (P), both, or none for controls. Overall, the fen samples showed higher carbon dioxide (CO2) and methane (CH4) production rates at 1, 5, 15, and 25 °C compared to the bog samples, which we attributed to differences in soil properties and initial microbial biomass. The bog sample with added N produced more CO2 compared to its control, while the fen sample with added P produced more CO2 compared to its control. It was unexpected that the addition of both N and P reduced CO2 but increased CH4 production in both soils compared to their controls. After a month, the pore water C, N, and P stochiometric ratios approached the initial soil microbial biomass ratios, suggesting microbial nutrient recycling in an inherently nutrient-poor soil environment. These preliminary results imply a complex response of carbon turnover in peatland soils to nutrient enrichment.
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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 | 0.000 |
| 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 | 0.000 |
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".