Phytogeographical Analysis of Seed Plant Genera in China
Bibliographic record
Abstract
BACKGROUND AND AIMS: A central goal of biogeography and ecology is to uncover and understand distributional patterns of organisms. China has long been a focus of attention because of its rich biota, especially with respect to plants. Using 290 floras from across China, this paper quantitatively characterizes the composition of floristic elements at multiple scales (i.e. national, provincial and local), and explores the extent to which climatic and geographical factors associated with each flora can jointly and independently explain the variation in floristic elements in local floras. METHODS: A study was made of 261 local floras, 28 province-level floras and one national-level flora across China. Genera of seed plants in each flora were assigned to 14 floristic elements according to their worldwide geographical distributions. The composition of floristic elements was related to climatic and geographical factors. KEY RESULTS AND CONCLUSIONS: Variations in percentages of cosmopolitan, tropical and temperate genera among local floras tend to be greater at higher latitudes than at lower latitudes. Latitude is strongly correlated with the proportions of 13 of the 14 floristic elements. Correlations of the proportions of floristic elements with longitude are much weaker than those with latitude. Climate represented by the first principal component of a principal component analysis was strongly correlated with the proportions of floristic elements in local floras (|r| = 0.75 +/- 0.18). Geographical coordinates independently explained about four times as much variation in floristic elements as did climate. Further research is necessary to examine the roles of water-energy dynamics, geology, soils, biotic interactions, and historical factors such as land connections between continents in the past and at present in creating observed floristic patterns.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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 teacher head, 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".