Potential Vulnerability of Deep Carbon Deposits of Forested Swamps to Drought
Bibliographic record
Abstract
Climate warming is resulting in increases in the frequency and intensity of summer droughts in the Great Lakes–St. Lawrence forest region (Ontario, Canada), raising concerns for the fate of C stores. We hypothesized that deeper peat historically existing beneath the water table would produce significant CO2 efflux during summer droughts. To test this hypothesis, we collected saturated peat cores, partitioned them into depth intervals, incubated the peat under conditions that resulted in peat drying, and monitored daily CO2 production together with potential drivers of CO2 production, including peat quality, microbial biomass, and microbial extracellular enzyme activity. Peat CO2 production (μmol CO2 min−1 g−1 dry soil) was highest in the top 30 cm of the peat profile, with the highest production at intermediate volumetric water content (VWC). Peat substrates fuelling CO2 production had quotients of C to N of <20 and were characterized by more labile forms of C. Microbial biomass C (mg C g−1 dry soil) and most microbial extracellular enzymes (nmol g−1 h−1) were also highest in the top 30 cm of the peat profile. Activities of microbial extracellular enzymes shifted in their contribution to CO2 production as the peat dried, with hydrolases positively related to CO2 under dry conditions (5–35%) and negatively under wet conditions (65 and 85%), with phenol oxidase showing the opposite pattern. Currently, the relatively poor quality (i.e., high C/N) of peat in catotelm limits rapid release of CO2 with water table declines. However, this substantial C store may be vulnerable to decomposition if constraints on quality are alleviated.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".