Physical fractionation of soil organic matter: Destabilization of deep soil carbon following harvesting of a temperate coniferous forest
Bibliographic record
Abstract
Developing a better understanding of the processes involved in controlling soil carbon (C) storage and turnover in native forest soils is critical if we are to fully understand the role land management activities play in the global C cycle. Separating soil organic matter (SOM) into discrete fractions has been successfully used to isolate changes in the structure and function of the SOM pool in response to land management activities but investigations in native forest systems are rare. Using a density fractionation procedure, we isolated and characterized three distinct SOM fractions (free, intra‐aggregate, and organo‐mineral) across a postharvest forest age sequence. We describe age related variations in each of these fractions with respect to their contribution to soil mass, C storage, C concentrations, C‐to‐N ratios, and δ13C ratios. In conceptual models of SOM pool structure, the organo‐mineral fraction is assumed to be largely stable. We show that harvesting may increase the potential for loss of soil C (i.e., destabilize the soil C pool) and that a significant portion of the soil C pool may be cycling on decadal timescales. Isotopic evidence is consistent with a period of C loss attributable to increased rates of decomposition, with losses below 20 cm driving the trend. We encourage investigators studying the effects of forest harvesting on SOM storage to consider the deeper mineral soil (20+ cm) and how we may increase SOM turnover time and stabilization capacity in a native forest system.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 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".