Soil carbon stocks, mineralization rates, and CO<sub>2</sub> effluxes under 10 tree species on contrasting soil types
Bibliographic record
Abstract
Afforestation can increase soil organic carbon (SOC) storage, but the selection of tree species may be critical. This study explored soil CO2 production and effluxes in relation to SOC contents in temperate forests. Sites included even-aged (38 years) first generation stands of eight coniferous and two deciduous species planted at three sites along a gradient in soil fertility. SOC stocks (forest floor + mineral soil 050 cm) differed significantly between tree species, but soil type influenced SOC stocks the most. SOC stocks were significantly higher for stands on low-fertility sandy soils (141 ± 31 Mg C·ha1) than stands on fertile loamy soils (62 ± 14 Mg C·ha1). Soil CO2 effluxes were measured on two occasions in 2002 and varied between 18.7 and 36.3 kg C·ha1·day1. Variability within temperature-adjusted fluxes was not clearly related to tree species or soil type. Potential CO2 production rates in a laboratory incubation experiment (reported as CO2 produced per unit soil C) were mainly affected by site and were two times higher for soils from the nutrient-rich site compared with the most nutrient-poor site. Thus, the combined effect of contrasting CO2 production rates and stocks of soil C may explain the similar CO2 effluxes measured in the field. Results suggest that selection of soil type and tree species are important for SOC sequestration in future afforestation projects.
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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".