Global Warming Impacts on Alpine Vegetation Dynamic in Qinghai-Tibet Plateau of China
Bibliographic record
Abstract
This study is to illustrate alpine vegetation dynamics in Qinghai-Tibetan Plateau of China from simulated filed experimental climate change, vegetation community dynamic simulation integrated with scenarios of global temperature increase of 1 to 3˚C, and simulated regional alpine vegetation distribution changes in responses to global warming.Our warming treatment increased air temperatures by 5˚C on average and soil temperatures were elevated by 3˚C at 5 cm depth.Aboveground biomass of grasses responded rapidly to the warmer conditions whereby biomass was 25% greater than that of controls after only 5 wk of experimental warming.This increase was accompanied by a simultaneous decrease in forb biomass, resulting in almost no net change in community biomass after 5 wk.Under warmed conditions, peak community bio-mass was extended into October due in part to continued growth of grasses and the postponement of senescence.The Vegetation Dynamic Simulation Model calculates a probability surface for each vegetation type, and then combines all vegetation types into a composite map, determined by the maximum likelihood that each vegetation type should distribute to each raster unit.With scenarios of global temperature increase of 1˚C to 3˚C, the vegetation types such as Dry Kobresia Meadow and Dry Potentilla Shrub that are adapted to warm and dry conditions tend to become more dominant in the study area.
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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 | 0.001 | 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".