Lake floodplains as sinks for stable soil organic carbon: is the carbon plant- or microbe-derived, and why does permanent land use matter?
Bibliographic record
Abstract
Abstract Floodplain ecosystems play a key role in soil organic carbon (SOC) storage, as they integrate inputs from both vegetation and sediments. Promoting land uses with low anthropogenic disturbances helps maintain the function of these ecosystems as soil carbon (C) sinks. However, the tipping points along a disturbance gradient where land use transitions generate the largest SOC losses or gains remain unclear, though they are key for effective land use management and climate change mitigation. Because flood events can negatively impact labile C, determining the main origin of stable SOC, whether from plants or soil microorganisms, is also important to identify the optimal combination of land use, vegetation, and soil type for SOC stabilization in floodplains. We examined how SOC storage and stabilization vary in the floodplain of Lake Saint-Pierre, Quebec, Canada along an anthropogenic disturbance gradient of six land uses: conventional and improved croplands, temporary and permanent meadows, marshes, and forested swamps. In all 6 land uses, we quantified SOC stocks, mineral-associated organic matter - C (MAOM-C), particulate organic matter - C (POM-C), soil δ 13 C and sugars. Land use effects on SOC storage and stabilization were most pronounced in the topsoil (0-10 cm), with forested swamps showing the highest vegetation biomass, SOC, and MAOM-C. Microbe-derived inputs were more abundant in MAOM, whereas plant-derived C dominated POM. SOC gains increased with decreasing disturbance, with a tipping point occurring in the transition from temporary to permanent meadows. Our results highlight the importance of conserving permanent and low-disturbance land uses to promote SOC persistence in floodplain ecosystems and emphasize the central role of microbial metabolism in stabilization processes.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".