Soil Properties Controlling the Adsorption of Dissolved Organic Carbon to Mineral Soils
Bibliographic record
Abstract
Dissolved organic C (DOC) is the most reactive and mobile component of soil C and can be retained within mineral soils by adsorption. We determined the adsorption characteristics for 52 mineral soil samples from 17 temperate and boreal soil profiles, using a modified Langmuir equation. The DOC solution used for batch experiments was derived from the organic horizons of a Spodosol. We analyzed the extent to which soil properties, such as the sum of poorly crystalline Fe and Al (Fe pc + Al pc ), texture, and soil C, are related to DOC adsorption to mineral soils. Sorption characteristics including the maximum adsorption capacity ( Q max ), and the null point (np), where adsorption equals desorption, were best explained by Fe pc + Al pc ( R 2 = 0.55 and 0.28, respectively). The Al pc exerted a stronger influence than Fe pc on Q max A simple method for estimating Q max was developed whereby the change in pOH after treatment with NaF is well correlated to Fe pc + Al pc ( R 2 = 0.71, P < 0.0001) and Q max ( R 2 = 0.50, P < 0.0001). The influence of clay content on Q max was of secondary importance and was largely masked by the dominant influence of Fe pc + Al pc Soil C did not have any influence on Q max , but a slight negative influence on np. The B horizons of Spodosols and volcanic soils had the greatest Q max , while large levels of soil C in Spodosols produced a high desorption potential. Results from this study emphasize the importance of considering the adsorption potential in conjunction with the desorption potential for better prediction of changes to the size of mineral soil C pools and DOC export to aquatic systems.
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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 | 0.001 | 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".