A continental-scale study of Spodosols across North America and implications for soil organic carbon dynamics
Bibliographic record
Abstract
Spodosols are among the most prevalent soil orders in temperate forests and play a key role in carbon sequestration and climate regulation. Podzolization promotes the accumulation and mobilization of mineral-associated organic carbon (MAOC) in deeper horizons, and the concentration of Fe Al oxyhydroxides controls the efficiency of this process and the carbon storage capacity of these soils. Yet, no large-scale study has compiled Spodosol data to evaluate soil properties, podzolization thresholds, and the influence of Fe Al oxyhydroxides and clays on MAOC storage. Here, we present a continental-scale compilation of over 3000 Spodosol profiles from across North America to assess SOC, Fe Al oxyhydroxides, clay content, and development thresholds based on parent material Fe content. We also estimated carbon saturation and maximum MAOC storage capacity using both silt + clay and Fe Al oxyhydroxide concentrations. Our results show that coastal regions exhibit exceptionally high SOC and Fe oxyhydroxide levels, while more continental regions have lower concentrations. Parent material Fe content was significantly higher in coastal regions, especially Coastal Alaska (12 %), compared to <4 % in continental areas. We demonstrate that concentration of Al and Fe oxyhydroxides is a more reliable approach of estimating MAOC storage capacity than silt + clay content. The mineralogical capacity of Spodosols, based on Fe Al oxyhydroxides, supports maximum MAOC concentrations ranging from ∼2 % in continental regions to nearly 10 % in Coastal Alaska. These findings highlight the role of climate in driving podzolization intensity and show that Spodosols, especially in northern coastal regions, hold significant potential for additional carbon sequestration..
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 | 0.001 | 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".