Response of soil CO<sub>2</sub> and O<sub>2</sub> concentrations to forest soil compaction at the Long-term Soil Productivity sites in central British Columbia
Bibliographic record
Abstract
The concentrations of soil CO2 and O2 at three Long-term Soil Productivity sites located in the Sub-Boreal Spruce biogeoclimatic zone of British Columbia, Canada, were monitored over several growing seasons. These sites were treated with three levels of soil compaction and three levels of organic material removal following forest harvest. Both compaction and depth had a significant effect upon soil CO2 concentrations and average values increased in response to deeper sampling and compaction. Removal of the forest floor and other organic materials had no significant effect upon soil CO2 and O2 concentrations. Mean soil CO2 concentrations varied from site to site and data collected over three growing seasons also showed that mean CO2 values in response to compaction fluctuated significantly from year to year. The high levels of soil CO2 observed in this study (up to 40 000 µ L L−1), especially in response to the compaction treatments, may have had a substantial effect upon whole plant carbon allocation as well as soil nutrition available to trees growing on the treated plots. Although mean soil O2 also decreased in response to compaction and sampling depth, these lower concentrations did not approach the levels required to influence plant growth. Key words: Soil, compaction, carbon dioxide, oxygen, long-term soil productivity
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".