Sequestration of carbon in the humus layer of Swedish forests — direct measurements
Bibliographic record
Abstract
To determine sequestration rates of carbon dioxide (CO2) we calculated the carbon (C) storage rate in humus layers of Swedish forests with Podsolic soils, which account for 14.2 × 106ha of the 22.7 × 106ha of forested land in Sweden. Our data set covered 41 years of humus inventories and mean humus layer thickness in 82 513 plots. We analysed three forest types: (i) all combinations of tree species, (ii) forests dominated (>70%) by Norway spruce ( Picea abies (L.) Karst.), and (iii) forests dominated (>70%) by Scots pine ( Pinus sylvestris L.). To relate changes in humus layer thickness to land area we used the intersections in 25 km × 25 km grids and used kriging interpolation, permitting calculations for each forest type. For each intersection mean humus thickness for each year was calculated and regressed against time to obtain the rate of change. This rate, humus bulk density, and humus C concentration were used to calculate sequestration rates. The mean sequestration rate was 251 kg C·ha–1·year–1, which is higher than theoretical values. The sequestration rate was positively related to temperature sum, albeit including effects of forest management. The pine-dominated forest type had a mean rate of 283 kg C·ha–1·year–1, and the spruce-dominated had a mean rate of 239 kg C·ha–1·year–1. Under similar site conditions, pine sequestered more C than spruce (difference of 71 kg C·ha–1·year–1; p < 0.0001), showing the importance of this type of ecosystem for C sequestration.
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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 | 0.002 | 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".