Soil organic carbon along a geothermal gradient in North-West Canada
Bibliographic record
Abstract
Global warming will increase soil microbial activity and thus catalyse the mineralisation of soil organic carbon (SOC). Predicting the dynamics of soil organic carbon in response to warming is crucial but associated with large uncertainties, owing to experimental limitations. Most studies use in-vitro incubation experiments or relatively short-term in-situ soil warming experiments. Long-term observations on the consequences of soil warming on whole-profile SOC are still rare. Here, we used a long-term geothermal gradient in North-West Canada to study effects of warming on quantity and quality of SOC in an aspen forest ecosystem. The Takhini hot springs are located within the region of discontinuous permafrost in the southern Yukon Territory, Canada. The springs warm the surrounding soil constantly and lead to a horizontal temperature gradient of approximately 10°C within a radius of 100 meters. As these natural springs heat the ground for centuries and the forest ecosystem surrounding the springs is relatively homogenous, the site provides ideal conditions for observing long-term effects of soil warming on ecosystem properties. Soils were sampled at four different warming intensities to a depth of 80 cm and analysed for their SOC content and further soil properties in different depths. For the bulk soil, we found a significant negative relationship between soil temperature and SOC stocks. This confirms that climate change will most likely induce SOC loss and thus a positive climate- carbon cycle feedback loop. The response of five different SOC fractions to warming will also be presented.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 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".