Complexity of nutrient enrichment on subarctic peatland soil CO2 and CH4 production 
Bibliographic record
Abstract
Wildfires are increasing across northern high latitudes. Besides the immediate carbon pool losses from directly disturbed areas, recent studies have reported high porewater nitrogen (N) and phosphorus (P) concentrations in burned areas and downstream waters for a few months to several years after fire occurrence. Increasing nutrient deposition and soil fertilizer use have been widely investigated for water quality and carbon loss in agricultural soils, but not for remote subarctic peatlands. In this study, we sampled soil cores (0-25 cm) from a bog and a fen peatland in the Scotty Creek watershed in the Northwest Territories and conducted an incubation experiment for the effects of added nutrients in porewater. Aliquots of the peatlands were divided into separate containers and artificial porewater was added, either amended with dissolved inorganic N (NH4 + NO3), P (PO4), both N and P, or unamended. The production rates of gaseous CO2, CH4 and N2O were measured at 1, 5, 15, and 25°C. We further analyzed the initial and final soil physical properties, porewater chemistry, and microbial biomass C:N:P ratios. The fen incubations yielded overall greater CO2 and CH4 production rates than the bog incubations, which we attributed to differences in soil properties and initial microbial biomass. The N addition to the bog samples increased CO2 production, while the P addition to the fen samples increased CO2 production. The addition of both N and P reduced CO2 production but elevated that of CH4 for both peatland soils. 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".