Organic matter, carbon, and nitrogen relationships of regreened forest soils in an industrially impacted landscape
Bibliographic record
Abstract
Context Soil organic matter (SOM) is largely composed of carbon (C) and nitrogen (N), the proportions of which often change with soil depth. The relationships between SOM, C, and N in forest soils can be greatly altered in degraded landscapes and understanding these relationships is integral for successful forest restoration planning. Aims The current study investigated SOM, C, and N relationships in highly degraded forest soils by depth following regreening (one-time application of soil amendments and afforestation). Additionally, the use of standard C:OM ratios (which are commonly used to estimate soil C) were assessed. Methods The SOM, C, and N were measured at five different depths, at nine sites, ranging in time since regreening treatment applications across one of the world’s largest regreening programmes in the City of Greater Sudbury, Canada. Key results The C:OM and C:N ratios decreased with soil depth while N:OM increased. The C and N were significantly correlated with SOM at all depths (excluding the L horizon). The C:OM ratio was lower than standard values and did not change between 16 and 41 years since the application of 10 Mg ha−1 of dolomitic limestone. Conclusions Despite massive soil degradation, SOM, C, and N relationships over soil depth at the regreening sites are consistent with unimpacted forest soils. Applying commonly used C:OM ratios drastically overestimated soil C pools, especially at lower depths. Implications Even in the most degraded landscapes, restoration can improve soil properties. Standard C:OM ratios should be used with caution.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".