Carbon and nitrogen stocks in Norwegian forest soils — the importance of soil formation, climate, and vegetation type for organic matter accumulation
Bibliographic record
Abstract
Relationships between soil C and N stocks and soil formation, climate, and vegetation were investigated in a gridded database connected to the National Forest Inventory in Norway. For mineral soil orders, C and N stocks were estimated to be 11.1–19.3 kg C·m−2 and 0.41–0.78 kg N·m−2, respectively, declining in the following order: Gleysols > Podzols > Brunisols > Regosols. Organic peat-type soils stored, on average, 31.3 kg C·m−2 and 1.10 kg N·m−2, whereas shallow Organic folisols stored, on average, 10.2 kg C·m−2 and 0.34 kg N·m−2. For Norway’s 120 000 km2 of forest, the total of soil C stocks was estimated to be 1.83 Gt C, with a 95% CI of 1.71–1.95 Gt C. Podzolic soils comprise the largest soil group and store approximately 50% of the forest soil C. Sixty percent of the soil C stock in Podzolic soils was stored in the mineral soil, increasing with temperature and precipitation. Poorly drained soil types store approximately 47% of the total forest soil C in Norway. Soils with water saturation have large C stocks mainly in the forest floor, suggesting that they are more susceptible to forest management and environmental change. Soil C stocks under pine and spruce forests were similar, although pine forests had larger C stocks in the forest floor, while spruce forests had the highest C stocks in the mineral soil compartment. C stocks in the forest floor increase from dry to moist ground vegetation, while ground vegetation nutrient classes reflect better the C and N stocks in the mineral soil.
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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 | 0.001 | 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 | 0.000 | 0.000 |
| 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".