Factors influencing mineralizable carbon in a landscape with variable topography
Bibliographic record
Abstract
Management-induced erosion has substantially increased the spatial variability in soil organic carbon (OC) stocks in landscapes with variable topography, but its impact on C dynamics is not well understood. The variability in cumulative C mineralization of samples from five positions in a landscape was examined in an incubation study and the effects of water content and depth within the A horizon were assessed. Mineralization was generally at a maximum at water contents between 80 and 95% water-filled pore space. The amount of C mineralized increased with OC content, but the proportion of the mineralizable OC decreased with increasing OC content. Few significant differences in C mineralization existed between surface and subsurface layers of the A horizon. The spatial patterns in maximum C mineralization were generally indicative of the patterns obtained when mineralization was calculated using spatial patterns of actual seasonal average water contents. This suggests the spatial pattern of maximum mineralization was not strongly influenced by variable hydrologic conditions on this site and reflects the spatial patterns in annual mineralization. The proportion of OC mineralized was not related to the proportion of the OC in particulate organic carbon (POC) or in the silt + clay fractions, but was significantly negatively related to the saturation of the clay + silt fraction with C. We speculate that the decline in the proportion of mineralized OC with increasing OC content reflects an increased proportion of the capacity of the silt + clay fraction to retain and physically protect OC within microaggregates. Key words: Erosion, deposition, mineralizable organic carbon, spatial variability
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".