Storage and Distribution of Organic Carbon and Nutrients in Acidic Soils Developed on Sulfidic Sediments: The Roles of Reactive Iron and Macropores
Bibliographic record
Abstract
In a boreal acidic sulfate-rich subsoil (pH 3-4) developing on sulfidic and organic-rich sediments over the past 70 years, extensive brownish-to-yellowish layers have formed on macropores. Our data reveal that these layers ("macropore surfaces") are strongly enriched in 1 M HCl-extractable reactive iron (2-7% dry weight), largely bound to schwertmannite and 2-line ferrihydrite. These reactive iron phases trap large pools of labile organic matter (OM) and HCl-extractable phosphorus, possibly derived from the cultivated layer. Within soil aggregates, the OM is of a different nature from that on the macropore surfaces but similar to that in the underlying sulfidic sediments (C-horizon). This provides evidence that the sedimentary OM in the bulk subsoil has been largely preserved without significant decomposition and/or fractionation, likely due to physiochemical stabilization by the reactive iron phases that also existed abundantly within the aggregates. These findings not only highlight the important yet underappreciated roles of iron oxyhydroxysulfates in OM/nutrient storage and distribution in acidic sulfate-rich and other similar environments but also suggest that boreal acidic sulfate-rich subsoils and other similar soil systems (existing widely on coastal plains worldwide and being increasingly formed in thawing permafrost) may act as global sinks for OM and nutrients in the short run.
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 | 0.000 |
| 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".