Biogeographical patterns of Chinese spiders (Arachnida: Araneae) based on a parsimony analysis of endemicity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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 B 2 : Tibetan Plateau and Inner Mongolia‐Xinjiang subregions), Central Northern region (clade B 3 ), Eastern Northern region (clade B 4 ), Central region (clade C 2 ), Eastern Southern region (clade C 3 ), Western Southern region (clade C 4 ), and Central Southern region (clade C 5 ). 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.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it