Bibliographic record
Abstract
Abstract. Peatlands pools are unvegetated, inundated depressions that cover up to 30 % of the surface of many temperate and boreal peatlands and that are net carbon (C) sources within C-accumulating ecosystems. The emission of carbon dioxide (CO2) and methane (CH4) from peatland pools comes from the degradation of organic matter (OM) that comprise the surrounding matrix. It is, however, not clear how decomposition rates in pools, which define their function and distinguish them from other aquatic ecosystems, vary spatially and what mechanisms drive these variations. We quantified rates of OM decomposition from fresh litter at different depths in six pools of distinct morphological characteristics in a temperate ombrotrophic peatland using litterbags of Sphagnum capillifolium and Typha latifolia over a 27-month period and measured potential CO2 and CH4 production of pool sediments in laboratory incubations. Rates of decomposition were faster for T. latifolia than S. capillifolium and, overall, faster at the pool surface and decreased with increasing depth. Pool sediment chemistry was variable among pools and drove the production of CH4 and CO2 from sediments, with decreasing CO2 production with increasing OM humification and decreasing CH4 production with increasing nitrogen-to-phosphorus ratio. Both CH4 and CO2 production from pool sediments were higher in the 1 m deep pools, but similar in the shallow < 1 m and the > 1.5 m deep pools. Our results show that OM decomposition in peatland pools is highly variable and related to OM chemistry, but decomposition depends primarily on the environmental conditions in which it occurs, with differences in both fresh litter and pool sediment decomposability as a function of decreasing O2 concentrations, light, and temperature with increasing depth in the pools.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.024 | 0.024 |
| Insufficient payload (model declined to judge) | 0.160 | 0.132 |
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".