Charcoal Distribution Affects Carbon and Nitrogen Contents in Forest Soils of California
Bibliographic record
Abstract
Fire is the dominant natural disturbance regime in most ecosystems of California. The long‐term relict of fire is charcoal, which has been shown to increase N mineralization and also represents a pool of chemically stabilized C whose quantity and spatial distribution have not been well characterized in forest soils. We examined the charcoal content in three different ecosystems of the Sierra Nevada, including oak woodland (low elevation), mixed conifer (middle elevation), and red fir (high elevation). Using a fine‐scale (2.5‐m minimum resolution) spatially explicit sampling protocol applied to plots ranging between 0.25 to 0.5 ha, we examined the autocorrelation of forest floor and mineral soil properties including charcoal C. Charcoal C content ranged from 1000 to 5000 kg ha −1 in the surface 6 cm of soil and increased with increasing elevation and latitude. Spatial patterns of forest floor and mineral soil properties were generally patchy at a scale of 5 to 20 m, except in the mixed conifer ecosystem (no pattern). The patchiness that existed at the other sites was largely a result of the distribution of total C and total N in the mineral soil. A spatial mixed‐effect ANOVA indicated that charcoal had a 10 to 20% effect on C and N contents in both forest floor and mineral soil surface horizons, independent of other parameters including ecosystem type and total C or N. These results provide evidence that charcoal has a relationship with soil C and N content, which may influence soil biogeochemistry.
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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 | 0.001 |
| 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".