Long-term development of above- and below-ground carbon stocks following land-use change in subalpine ecosystems of the Swiss National Park
Bibliographic record
Abstract
Vegetation changes following agricultural land abandonment at high elevation — which is frequent in Europe — could have a major impact on carbon (C) sequestration. However, most information on the effects of vegetation changes on ecosystem C stocks originates from low-elevation studies on reforestation or early successional forests, and little is known about how these stocks change during long-term secondary forest succession and at high elevation. We assessed aboveground, belowground, and ecosystem organic matter and C stocks in high-elevation ecosystems that represent the long-term development (centuries) following land abandonment: short- and tall-grass pastures, Swiss mountain pine (Pinus mugo Turra), mixed-conifer, and Swiss stone pine ( Pinus cembra L.) – European larch ( Larix decidua P. Mill.) forests. Aboveground C stocks were lowest in the short-grass pastures (0.1 Mg C·ha–1) and reached a maximum in the mixed-conifer and stone pine – larch forests (166 Mg C·ha–1). Belowground C stocks did not differ among the ecosystems studied. We only detected ecosystem C sequestration during reforestation; whereas no significant differences in ecosystem C stocks were found during long-term secondary forest development. Our calculations showed that only an additional 1733–3032 Mg C·year–1 would be sequestered owing to natural reforestation in high-elevation Switzerland, which likely can be considered negligible compared with total annual C sequestration calculated for Swiss forests in other studies.
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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 | 0.001 |
| 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) | 0.001 | 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".