Features and distribution patterns of Chinese endemic seed plant species
Bibliographic record
Abstract
Abstract We compiled and identified a list of Chinese endemic seed plant species based on a large number of published references and expert reviews. The characters of these seed plant species and their distribution patterns were described at length. China is rich in endemic seed plants, with a total of 14 939 species (accounting for 52.1% of its total seed plant species) belonging to 1584 genera and 191 families. Temperate families and genera have a significantly higher proportion of endemism than cosmopolitan and tropical ones. The most primitive and derived groups have significantly higher endemism than the other groups. The endemism of tree, shrub, and liana or vine is higher than that of total species; in contrast, the endemism of herb is lower than that of total species. Geographically, these Chinese endemic plants are mainly distributed in Yunnan and Sichuan provinces, southwest China. Species richness and proportion of these endemic plants decrease with increased latitude and have a unimodal response to altitude. The peak value of proportion of endemism is at higher altitudes than that of total species and endemic species richness. The proportions of endemic shrub, liana or vine, and herb increase with altitude and have a clear unimodal curve. In contrast, the proportion of tree increases with altitude, with a sudden increase at ∼4000 m and has a completely different model. To date, our study provides the most comprehensive list of Chinese endemic seed plant species and their basic composition and distribution features.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".