Are vegetation–soil systems drivers of ecosystem carbon contents along an elevational gradient in a highland temperate forest?
Bibliographic record
Abstract
Vegetation–soil systems differentially influence the ecosystem processes related to the carbon cycle, particularly when one tree species is dominant over wide geographic regions that are undergoing climate change. The objective of this study was to quantify the stocks of ecosystem carbon in three vegetation–soil systems along a highland elevational gradient in central Mexico. The vegetation–soil systems, from lower to higher elevation, were dominated by Alnus jorullensis Kunth, Abies religiosa (Kunth) Schltdl. & Cham., and Pinus hartwegii Lindl., respectively. Above- and below-ground tree biomass was determined in each system, along with the litter, coarse woody material, roots, and litterfall. The A. religiosa system had the greatest stock of aboveground biomass carbon (216 ± 31 Mg C·ha−1). The A. jorullensis system had the greatest production of litterfall (3.1 ± 0.08 Mg·ha−1·year−1); however, the carbon content of this litter layer (1.2 ± 0.32 Mg C·ha−1) was lower than that of P. hartwegii (10.1 ± 0.28 Mg C·ha−1). Thus, the litter layer in the A. jorullensis system had markedly the shortest residence time (8 years), suggesting high rates of litter decomposition. The soil carbon stock (at soil depth of 1 m) was greater in A. jorullensis (189 Mg C·ha−1) and P. hartwegii (137 Mg C·ha−1) than in A. religiosa (68 Mg C·ha−1). The A. religiosa and A. jorullensis systems had the highest and lowest total ecosystem C content (301 and 228 Mg C·ha−1, respectively). Upward migration of the A. religiosa system in response to global climate change, however, could cause losses by 2030 of 187 Mg C·ha−1 associated with aboveground biomass.
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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 | 0.001 |
| 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".