Relationships between vegetation and stomata, and between vegetation and pollen surface soil in Yunnan, Southwest China
Bibliographic record
Abstract
Surface pollen and stomata of 61 samples collected in a study area ranging from tropical seasonal rainforest to oak forest ( Quercus spinosa ) in the Yulong Snow Mountain region in Yunnan, China, are used to distinguish vegetation communities. The results show that tropical seasonal rainforest (and mountain rainforest), south subtropical evergreen broad-leaved forest, and Quercus shrub are distinguished effectively from other vegetation types by analysis of surface pollen. The south subtropical evergreen broad-leaved forest, Pinus kesiya forest and evergreen broadleaf forest are distinguished effectively from other types of vegetation by pollen analysis. However, P. yunnanensis forest is not distinguished from other vegetation types, and P. armandii, P. densata forest and temperate deciduous conifer mixed forest are not distinguished. The over-representation of Pinus pollen is the main reason that these vegetation communities are not distinguished from each other. Conifer stomata analysis is an effective tool for identifying and distinguishing different types of coniferous forest, and this method performs well even with a small number of sampling points.
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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) | 0.000 | 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".