Long-term recovery of soil carbon stocks and permafrost depth lags recovery of organic layer thickness following fire in black spruce forests of the Copper River Basin, Alaska
Bibliographic record
Abstract
This study investigated differences in forest structure, organic layer thickness, soil organic carbon, and permafrost depth between late-successional (LS) and postfire (PF; 90–120 years since burn) plots under black spruce (Picea mariana (Mill.) B.S.P.) growing on fine-textured, poorly drained lacustrine sediments in the Copper River Basin, Alaska. Although live stem and seedling density and organic layer thickness were not significantly different between PF and LS plots (28 ± 7 cm and 31 ± 10 cm, respectively), we did find a significant difference in soil organic carbon (SOC) stocks (30 ± 10 kg·m−2 and 46 ± 12 kg·m−2, respectively) permafrost depth (90 ± 28 cm and 56 ± 12 cm, respectively). OLT was linearly related to 1 m SOC stocks for LS plots but not for PF plots, and LS plots had a greater proportion of highly decomposed (humic) material in the organic layer. The soil properties of PF plots appear to be on a trajectory of recovery toward those of LS plots with respect to SOC stocks, permafrost depth, and organic layer composition. However, PF plots remain different despite nearly 100 years since fire disturbance and thus they are potentially more sensitive to changes in future fire frequency or climate.
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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".