Controls on carbon accumulation and storage in the mineral subsoil beneath peat in Lakkasuo mire, central Finland
Bibliographic record
Abstract
Summary What processes control the accumulation and storage of carbon (C) in the mineral subsoil beneath peat? To find out we investigated four podzolic mineral subsoil profiles from forest and beneath peat in Lakkasuo mire in central boreal Finland. The amount of C in the mineral subsoil ranged from 3.9 to 8.1 kg m −2 over a thickness of 70 cm and that in the organic horizons ranged from 1.8 to 144 kg m −2 . Rates of increase of subsoil C were initially large (14 g m −2 year −1 ) as the upland forest soil was paludified, but decreased to < 2 g m −2 year −1 from 150 to 3000 years. The subsoils retained extractable aluminium (Al) but lost iron (Fe) as the surrounding forest podzols were paludified beneath the peat. A stepwise, ordinary least‐squares regression indicated a strong relation ( R 2 = 0.91) between organic C concentration of 26 podzolic subsoil samples and dithionite–citrate–bicarbonate‐extractable Fe (negative), ammonium oxalate‐extractable Al (positive) and null‐point concentration of dissolved organic C (DOC np ) (positive). We examined the ability of the subsoil samples to sorb dissolved organic C from a solution derived from peat. Null‐point concentration of dissolved C (DOC np ) ranged from 35 to 83 mg l −1 , and generally decreased from the upper to the lower parts of the profiles (average E, B and C horizon DOC np concentrations of 64, 47 and 42 mg l −1 ). The DOC np was positively correlated with percentage of soil C and silt and clay content. The concentration of dissolved organic C in pore water in the peat ranged from 12 to 60 mg l −1 (average 33 mg l −1 ), suggesting that the sorptive capacity of the subsoil horizons for C had been exhausted. We suggest that the increase of C contents in the subsoil beneath mires is related to adsorption of dissolved organic C and slow mineralization under anaerobic conditions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".