Depth Distribution and Predictors of Soil Organic Carbon in Podzols of a Forested Watershed in Southwestern Canada
Bibliographic record
Abstract
Forest soils of coastal British Columbia, Canada, may store significant amounts of organic matter because of the cool climate and high forest productivity of the area. The objectives of this study were to determine the distribution of soil organic carbon (SOC) in the profile and to identify the most important predictors of SOC in Podzols of a forested watershed in southwestern British Columbia. We sampled 9 soil profiles in undisturbed forest plots by morphological horizon and measured SOC using a dry combustion method. We also determined soil pH, texture, moisture content, total nitrogen, loss on ignition, and pyrophosphate- and oxalate-extractable Fe and Al. The average soil profile stored 15.9 kg C/m2 over a depth of 100 cm, which is higher than SOC stocks estimates for inland Canadian forests. The organic layer (LFH) only accounted for one fourth of the C stock. Sixty percent of the profile SOC (including the forest floor) was found in the subsoil of depth greater than 20 cm. Studies of SOC dynamics that only sample the topsoil are therefore inappropriate. Although the clay concentration was low (∼5%), the clay fraction accounted for one third of SOC. This suggests that organo-mineral interactions were an important factor for SOC storage. The major predictors of SOC in the mineral horizons were organically complexed Al and Fe and short-range order inorganic material. Crystalline clays also seemed to play a role in organic matter accumulation, but were not as important as poorly crystalline compounds. In the organic layer, organically complexed Fe forms correlated negatively with SOC, indicating that the amount of Fe available for adsorption to organic matter is limited. Organically complexed Al did not show the same negative association, suggesting the existence of a mechanism for upward translocation of Al into the FH horizon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".