Linking variability in soil solution dissolved organic carbon to climate, soil type, and vegetation type
Bibliographic record
Abstract
Abstract Lateral transport of carbon plays an important role in linking the carbon cycles of terrestrial and aquatic ecosystems. There is, however, a lack of information on the factors controlling one of the main C sources of this lateral flux, i.e., the concentration of dissolved organic carbon (DOC) in soil solution across large spatial scales and under different soil, vegetation, and climate conditions. We compiled a database on DOC in soil solution down to 80 cm and analyzed it with the aim, first, to quantify the differences in DOC concentrations among terrestrial ecosystems, climate zones, soil, and vegetation types at global scale and second, to identify potential determinants of the site‐to‐site variability of DOC concentration in soil solution across European broadleaved and coniferous forests. We found that DOC concentrations were 75% lower in mineral than in organic soil, and temperate sites showed higher DOC concentrations than boreal and tropical sites. The majority of the variation (R2 = 0.67–0.99) in DOC concentrations in mineral European forest soils correlates with NH4+, C/N, Al, and Fe as the most important predictors. Overall, our results show that the magnitude (23% lower in broadleaved than in coniferous forests) and the controlling factors of DOC in soil solution differ between forest types, with site productivity being more important in broadleaved forests and water balance in coniferous stands.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".