Depth-Dependent Mineral Soil CO2 Production Processes: Sensitivity to Harvesting-Induced Changes in Soil Climate
Bibliographic record
Abstract
Forest harvesting induces a step change in the climatic variables (temperature and moisture), that control carbon dioxide (CO2) production arising from soil organic matter decomposition within soils. Efforts to examine these vertically complex relationships in situ within soil profiles are lacking. In this study we examined how the climatic controls on CO2 production change within vertically distinct layers of the soil profile in intact and clearcut forest soils of a humid temperate forest system of Atlantic Canada. We measured mineral soil temperature (0, 5, 10, 20, 50 and 100 cm depth) and moisture (0-15 cm and 30-60 cm depth), along with CO2 surface efflux and subsurface concentrations (0, 2.5, 5, 10, 20, 35, 50, 75 and 100 cm depth) in 1 m deep soil pits at 4 sites represented by two forest-clearcut pairs over a complete annual cycle. We examined relationships between surface efflux at each site, and soil heat, moisture, and mineral soil CO2 production. Following clearcut harvesting we observed increases in temperature through depth (1-2°C annually; often in excess of 4°C in summer and spring), alongside increases in soil moisture (30%). We observed a systematic breakdown in the expected exponential relationship between CO2 production and heat with mineral soil depth, consistent with an increase in the role moisture plays in constraining CO2 production. These findings should be considered in efforts to model and characterize mineral soil organic matter decomposition in harvested forest soils.
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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) | 0.000 | 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".