Carbon sequestration in a chronosequence of Scots pine stands in a reclaimed opencast oil shale mine
Bibliographic record
Abstract
Ecosystems that develop on mine spoil can serve as significant sinks for CO2. The aim of this study was to estimate the rate of carbon accumulation and its distribution along forest ecosystem partitions in young Scots pine ( Pinus sylvestris L.) plantations in the Narva oil shale opencast, Estonia. The tree layer was measured in 2004 in 13 stands afforested with 2-year-old seedlings during 1968 to 1994. Three stands (afforested in 1990, 1983, and 1968) were selected for detailed analysis of the carbon sequestration. Soil profiles were sampled in these stands in 2005. Radiocarbon analysis combined with a simple model of litter production was used to differentiate between plant-derived recent carbon and carbon stemming from fragments of oil shale. Total carbon accumulated since afforestation in vegetation, forest floor, and A horizon was 7.8 t·ha–1in the stand established in 1990, 34.5 t·ha–1in that established in 1983, and 133.4 t·ha–1in that established in 1968. Most of the sequestered carbon was allocated to tree stems; their portion increasing with age from 28% to 51%. The portion of recent soil organic carbon increased from 5% to 23%, which shows that soils contribute significantly to carbon accumulation during early forest succession on degraded land.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 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".