Long‐term geothermal warming reduced stocks of carbon but not nitrogen in a subarctic forest soil
Bibliographic record
Abstract
Abstract Global warming is accelerating the decomposition of soil organic matter (SOM). When predicting the net SOM dynamics in response to warming, there are considerable uncertainties owing to experimental limitations. Long‐term in situ whole‐profile soil warming studies are particularly rare. This study used a long‐term, naturally occurring geothermal gradient in Yukon, Canada, to investigate the warming effects on SOM in a forest ecosystem. Soils were sampled along this thermosequence which exhibited warming of up to 7.7℃; samples were collected to a depth of 80 cm and analysed for soil organic carbon (SOC) and nitrogen (N) content, and estimates made of SOC stock and fractions. Potential litter decomposition rates as a function of soil temperature and depth were observed for a 1‐year period using buried teabags and temperature loggers. The SOC in the topsoil (0–20 cm) and subsoil (20–80 cm) responded similar to warming. A negative relationship was found between soil temperature and whole‐profile SOC stocks, with a total loss of 27% between the warmest and reference plots, and a relative loss of 3%℃−1. SOC losses were restricted to the particulate organic matter (POM) and dissolved organic carbon (DOC) fractions with net whole‐profile depletions. Losses in POM‐C accounted for the largest share of the total SOC losses. In contrast to SOC, N was not lost from the soil as a result of warming, but was redistributed with a relatively large accumulation in the silt and clay fraction (+40%). This suggests an immobilization of N by microbes building up in mineral‐associated organic matter. These results confirm that soil warming accelerates SOC turnover throughout the profile and C is lost in both the topsoil and subsoil. Since N stocks remained constant with warming, SOM stoichiometry changed considerably and this in turn could affect C cycling through changes in microbial metabolism.
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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".