Biogeographical patterns of Chinese spiders (Arachnida: Araneae) based on a parsimony analysis of endemicity
Bibliographic record
Abstract
Abstract Aim The distributions of Chinese spiders are used to form biotic regions and to infer biogeographical patterns. Location China. Methods China was initially divided into 294 quadrats of 2° latitude by 2° longitude. The distributions of 958 species of spiders were summarized for each quadrat. Subsequently, these quadrats were pooled into 28 areas based on topographical characteristics and to a lesser extent on the distributions of spiders. Parsimony analysis of endemicity (PAE) was used to classify the 28 areas based on the shared distributional patterns of spiders. Results China was found to have seven major biogeographical regions based on the distributional patterns of spiders: Western Northern region (clade B2: Tibetan Plateau and Inner Mongolia‐Xinjiang subregions), Central Northern region (clade B3), Eastern Northern region (clade B4), Central region (clade C2), Eastern Southern region (clade C3), Western Southern region (clade C4), and Central Southern region (clade C5). Main conclusions The distributional patterns of Chinese spiders correspond broadly to geological provinces. A comparison of the geological provinces and the distributional patterns of spiders reveals that the spiders occur south of the geological provinces. Furthermore, a general biogeographical classification with five natural areas is suggested as follows: Tibetan Plateau, Central Northern, Eastern Northern, Western Northern (excluding Tibetan Plateau), and Southern regions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".