Organic carbon stocks, quality and prediction in permafrost-affected forest soils in North Canada
Bibliographic record
Abstract
High-latitude soils store a large amount of the global soil organic carbon (SOC). The SOC stocks in mineral soils under different permafrost conditions, however, are underrepresented in global carbon databases. We sampled mineral forest soils under continuous and discontinuous to sporadic permafrost conditions on the Canadian Boreal and Taiga Plain. We determined the SOC stocks in the upper 60 cm of 94 soil pits across eleven sites (5–9 pits per site) and SOC quality using 13C isotopic signatures, C:N ratios and composition of aliphatic/aromatic and cellulose/lignin-like compounds obtained from mid-infrared spectra analyses. Lastly, we evaluated the prediction of SOC stocks in these soils using mid-infrared spectra and partial least square regression modelling (PLSR). The SOC stocks were on average four times higher in soils under continuous permafrost conditions (93.7–203.8 Mg SOC ha−1 in 0–45 cm) compared to soils under discontinuous to sporadic permafrost conditions (26.7–60.2 Mg SOC ha−1 in 0–60 cm). In addition, the SOC stocks were larger at moist and wet locations compared to dryer locations and varied significantly between sites, stressing the importance of small-scale geomorphic differences in controlling SOC in boreal mineral forest soils. Continuous permafrost SOC had a lower degree of decomposition compared to soils under discontinuous and sporadic permafrost. This indicates a potentially large proportion of SOC in boreal mineral soils to be vulnerable to warming associate increases in decomposition. The combination of mid-infrared with PLSR was suitable to predict the SOC stocks (R2 > 0.8) with an average uncertainty of 14–23%, which was less than the observed spatial variability of the field replicates (29–41%). Mid-infrared spectroscopy can thus offer an alternative to fill SOC data gaps of high latitude mineral forest soils and reduce uncertainties originating from the limited number of currently available SOC observations of Canadian boreal mineral forest soils.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".