Distribution of Soil Organic Carbon in Riparian Forest Soils Affected by Frequent Floods (Southern Québec, Canada)
Bibliographic record
Abstract
Measuring soil organic carbon (SOC) in riparian forest soils affected by floods is crucial for evaluating their concentration and distribution along hydrological gradients (longitudinal and transversal). Hydromorphological factors (e.g., sedimentation vs. erosion, size of floodplain, flood recurrence) may be the cause of major variations in the concentration of organic matter and SOC in soils and could have a direct impact on C levels in soil profiles. For this study, SOC concentrations were assessed in riparian soils collected along transects perpendicular to the riverbanks which cross through inundated and non-inundated zones. Other soil properties (e.g., acidity, nitrogen, texture, bulk density) that may affect the concentration of SOC were also considered. The main purpose of this study was to assess SOC concentrations in soils subjected to flooding with those outside the flood zones, and also measure various soil properties (in surface soils and at various depths ranging from 0 to 100 cm) for each selected area. Across the various areas, SOC shows marked differences in concentration and spatial distribution, with the lowest values found in mineral soils affected by successive floods (recurrence of 0–20 years). SOC at 0–20 cm in depth was significantly lower in active floodplains (Tukey HSD test), with average values of 2.29 ± 1.64% compared to non-inundated soils (3.83 ± 2.22%). The proportion of C stocks calculated in soils (inundated vs. non-inundated zones) was significantly different, with average values of 38.22 ± 10.40 and 79.75 ± 29.47 t·ha−1, respectively. Flood frequency appears to be a key factor in understanding the low SOC concentrations in floodplain soils subjected to high flood recurrence (0–20 years).
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".