Soil carbon stabilization along climate and stand productivity gradients in black spruce forests of interior Alaska
Bibliographic record
Abstract
The amount of soil organic carbon (SOC) in stable, slow-turnover pools is likely to change in response to climate warming because processes mediating soil C balance (net primary production and decomposition) vary with environmental conditions. This is important to consider in boreal forests, which constitute one of the world's largest stocks of SOC. We investigated changes in soil C stabilization along four replicate gradients of black spruce productivity and soil temperature in interior Alaska to develop empirical relationships between SOC and stand and physiographic features. Total SOC harbored in mineral soil horizons decreased by 4.4 g C·m2 for every degree-day increase in heat sum within the organic soil across all sites. Furthermore, the proportion of relatively labile light-fraction (density <1.6 g·cm3) soil organic matter decreased significantly with increased stand productivity and soil temperature. Mean residence times of SOC (as determined by Δ14C) in dense-fraction (>1.6 g·cm3) mineral soil ranged from 282 to 672 years. The oldest SOC occurred in the coolest sites, which also harbored the most C and had the lowest rates of stand production. These results suggest that temperature sensitivities of organic matter within discrete soil pools, and not just total soil C stocks, need to be examined to project the effects of changing climate and primary production on soil C balance.
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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 | 0.001 | 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".