Four centuries of soil carbon and nitrogen change after stand-replacing fire in a forest landscape in the western Cascade Range of Oregon
Bibliographic record
Abstract
Episodic stand-replacing wildfire is a significant disturbance in mesic and moist Douglas-fir ( Pseudotsuga menziesii (Mirb.) Franco) forests of the Pacific Northwest. We studied 24 forest stands with known fire histories in the western Cascade Range in Oregon to evaluate long-term impacts of stand-replacing wildfire on carbon (C) and nitrogen (N) pools and dynamics within the forest floor (FF, Oe and Oa horizons) and the mineral soil (0–10 cm). Twelve of our stands burned approximately 150 years ago (“young”), and the other 12 burned approximately 550 years ago (“old”). Forest floor mean C and N pools were significantly greater in old stands than young stands (N pools: 1823 ± 132 kg·ha–1vs. 1450 ± 98 kg·ha–1; C pools: 62 980 ± 5403 kg·ha–1vs. 49 032 ± 2965 kg·ha–1, mean ± SE) as a result of significant differences in FF mass. Forest floor C and N concentrations and C/N ratios did not differ by time since fire, yet potential N mineralization rates were significantly higher in FF of old sites. Old and young mineral soils did not differ significantly in pools, concentrations, C/N ratios, or cycling rates. Our results suggest that C and N are sequestered in FF of Pacific Northwest Douglas-fir forests over long (∼400 year) intervals, but that shorter fire return intervals may prevent that accumulation.
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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 | 0.001 | 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".