Soil Carbon Stocks and Carbon Stability in a Twenty‐Year‐Old Temperate Plantation
Bibliographic record
Abstract
Afforestation and reforestation are considered important tools for mitigating fossil fuel emission; however, establishment of plantations necessarily involves several silvicultural treatments that may influence soil organic C sequestration and, potentially, its relative stability. An experimental design established 20 yr ago, consisting of plantations of white pine ( Pinus strobus L.) and white spruce [ Picea glauca (Moench) Voss] was used to determine midterm impacts of blade scarification, fertilization, application of an herbicide, and tree species on soil C stocks and on the fraction of labile C determined by measurement of C mineralization on laboratory incubations. Twenty years after treatment, blade scarification had the greatest effect on soil organic C stock and stability. Carbon content was 54.2% lower in the F/H layer (equivalent to Oi and Oe/Oa layers) of blade‐scarified plots compared with plots without blade scarification. Effects on the mineral soil layers were less obvious and partly mitigated by the addition of fertilizer on the surface. Blade scarification affected the general quality of C in the F/H layer because it significantly increased by 21.1% the fraction of labile C to total C; nevertheless, labile C content was 51.9% lower in the blade‐scarified treatment. Vegetation control by herbicide as well as tree species had minor effects on C stocks and stability. A 9.0% decrease in mineral soil C content was observed with vegetation control. The F/H layer C concentration was 18.6% higher under white spruce than under white pine but these differences did not lead to a difference in C content. The small effects of treatments on surface mineral soil C could be explained by the limited capacity of this coarse‐textured soil to sequester more silt‐ and clay‐associated C.
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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 | 0.000 | 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".