How surface fire in Siberian Scots pine forests affects soil organic carbon in the forest floor: Stocks, molecular structure, and conversion to black carbon (charcoal)
Why this work is in the frame
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Bibliographic record
Abstract
In boreal forests, fire is a frequent disturbance and converts soil organic carbon (OC) to more degradation‐resistant aromatic carbon, i.e., black carbon (BC) which might act as a long‐term atmospheric‐carbon sink. Little is known on the effects of fires on boreal soil OC stocks and molecular composition. We studied how a surface fire affected the composition of the forest floor of Siberian Scots pine forests by comparing the bulk elemental composition, molecular structure ( 13 C‐MAS NMR), and the aromatic carbon fraction (BC and potentially interfering constituents like tannins) of unburned and burned forest floor. Fire reduced the mass of the forest floor by 60%, stocks of inorganic elements (Si, Al, Fe, K, Ca, Na, Mg, Mn) by 30–50%, and of OC, nitrogen, and sulfur by 40–50%. In contrast to typical findings from temperate forests, unburned OC consisted mainly of (di‐)O‐alkyl (polysaccharides) and few aromatic structures, probably due to dominant input of lichen biomass. Fire converted OC into alkyl and aromatic structures, the latter consisting of heterocyclic macromolecules and small clusters of condensed carbon. The small cluster size explained the small BC concentrations determined using a degradative molecular marker method. Fire increased BC stocks (16 g kg −1 OC) by 40% which translates into a net‐conversion rate of 0.7% (0.35% of net primary production) unburned OC to BC. Here, however, BC was not a major fraction of soil OC pool in unburned or burned forest floor, either due to rapid in situ degradation or relocation.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 it