SEASONAL AND INTER-ANNUAL DECOMPOSITION, MICROBIAL BIOMASS, AND NITROGEN DYNAMICS IN A CANADIAN BOG
Bibliographic record
Abstract
To establish the temporal and spatial variability of substrate contribution to ecosystem respiration (ER), we measured the seasonal and inter-annual microbial carbon dioxide (CO2) production potential, microbial biomass, and nitrogen dynamics over a period of 2 years in the upper 30 cm of a peat bog in southern Ontario. Samples collected during a warmer year with lower average summer water table position had larger inorganic and organic nitrogen (N) concentrations and microbial CO2 production potentials. Across all sampling dates, the distance of the water table beneath the surface was significantly and positively correlated with N availability, and in turn N availability was significantly and positively correlated with CO2 production, although direct correlation between water table position and CO2 production was only significant at P = 0.1. Inter-seasonal variability in CO2 production, microbial biomass, or N did not follow consistent patterns between years, and inorganic N species, particularly nitrate, concentrations varied relatively the most between sampling dates, although concentrations were always small relative to microbial biomass N and potassium sulfate- extractable organic N. Microbial CO2 production from the surface peat profile was calculated to be between 2.5 and 5.7 g CO2 m−2 day−1. Data extrapolation showed that microbial production of CO2 can be between 41 and 67% of the CO2 emitted as ER with the larger value falling in a warmer, drier year and that inter-annual changes in production potentials may partially explain increased ER in warmer, drier years. These results suggest that changes in microbial CO2 production and microbial community and nutrient characteristics may play an important role in controlling the emission of CO2 from terrestrial ecosystems such as peatlands.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".