Recovery of carbon pools a decade after wildfire in black spruce forests of interior Alaska: effects of soil texture and landscape position
Bibliographic record
Abstract
We measured organic-layer (OL) recovery and carbon stocks in dead woody debris a decade after wildfire in black spruce (Picea mariana (Mill.) B.S.P.) forests of interior Alaska. Previous study at these research plots has shown the strong role that landscape position plays in governing the proportion of OL consumed during fire and revegetation after fire. Here, we show that landscape position likely influences fire dynamics in these stands through changes in mineral soil texture. The content of fine-textured materials in underlying mineral soils was positively related to OL depths measured 1 and 10 years after fire, and there was an interaction between soil texture and elevation in governing OL consumption and OL recovery a decade following fire. OL depths 10 years after fire were 2 cm greater than 1 year after fire, with a range of 19 cm of accumulation to 9 cm of subsidence. Subsidence was inversely related to the percentage of fine textures within the parent material. The most influential factor determining the accumulation of OL carbon stocks a decade following wildfire was the interaction between landscape position and the presence of fine-textured soil. As such, parent material texture interacted with biological processes to govern the recovery of soil organic layers.
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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 | 0.000 | 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".