The responses of early foliar litter humification to reduced snow cover during winter in an alpine forest
Bibliographic record
Abstract
Ni, X., Yang, W., Li, H., Xu, L., He, J., Tan, B. and Wu, F. 2014. The responses of early foliar litter humification to reduced snow cover during winter in an alpine forest. Can. J. Soil Sci. 94: 453–461. Snow cover can be reduced by ongoing winter warming in alpine biomes, affecting foliar litter humification, but few reports are available. To quantitatively clarify how early foliar litter humification responds to reduced snow cover in winter, a field litterbag experiment was conducted in an alpine forest in southwestern China. Mass losses, ΔlogK, E4/E6, degrees of humification and humification rates of six typical local foliar litters were investigated at the snow formation, snow cover and snow melt stage under snowpack levels differing in depth (deep snowpack, medium snowpack, thin snowpack, no snowpack) from November 2012 to April 2013. The results indicated that 14–15% of willow (Salix paraplesia), 8–9% of fir (Abies faxoniana), 6–7% of birch (Betula albo-sinensis), 5–8% of cypress (Sabina saltuaria), larch (Larix mastersiana) and azalea (Rhododendron lapponicum) foliar litter was humified, which was about 50% of what decomposed during the first winter. Moreover, the early humification of foliar litter (except for fir and birch) responded positively to the reduced snow cover, but mass loss exhibited negative responses. Such results suggest that reduced snow cover in winter would increase soil carbon or other material sequestration in the scenario of climate change.
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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 | 0.000 | 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".