Assessing the Physical Stability of Soil Organic Carbon in Roadside Ecosystems
Bibliographic record
Abstract
Understanding the factors controlling the stability of soil organic carbon stocks, notably in urban areas such as roadsides, can contribute to a better quantification of the ecosystem services that these areas can provide, a key to improving urban planning and management. This study assessed soil carbon stability based on physical fractions in roadside ecosystems of southern Quebec, Canada. We measured the carbon content of soil mineral-associated (MAOC) and particulate (POC) organic carbon physical fractions of roadsides with different land uses and investigated relationships with road density, soil concentration of heavy metals, and soil salinity. We used the MAOC/POC ratio to evaluate the carbon storage potential of each physical fraction. The stable physical fraction MAOC contained a higher carbon content than the labile soil fraction POC across different depths. The MAOC/POC ratio was higher for sites with a more recent history of agriculture abandonment. MAOC was positively linked to road density, soil salinity, and heavy metal concentration. This study suggested that roadside soils have a high capacity to store carbon in a stable form. Additionally, the chemical properties of roadside soils did not adversely affect the physical stability of soil carbon, especially in the top mineral soil.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".