Do interlinks between geography and ecology explain the latitudinal diversity patterns in Sciuridae? An approach at the genus level
Bibliographic record
Abstract
The latitudinal gradient theory explains the uneven distribution of taxa richness across the world. We explore this theory using genera of Sciuridae (Mammalia: Rodentia). Distribution data for each genus were obtained from literature and mapped with the WorldMap program. The two hemispheres were subdivided into 23 latitudinal bands of equal area. As the total number of genera in each latitudinal band was influenced by the different available area, data were normalized prior to analyses. Then, genera density of each latitudinal band was correlated with latitude, and the ratio of genera richness of each guild to total genera richness was calculated for each latitudinal band. Total genus density was significantly correlated with flying squirrel density and terrestrial squirrel density in both hemispheres, and these two genera densities were significantly correlated with each other in the northern hemisphere. The guilds showed clear vicariance patterns. The total diversity of genera of Sciuridae was inversely correlated to latitude. The increase of genera towards tropical northern hemisphere was due to the progressive increase of the tree and flying squirrel genera. Change in biomes (tundra vs. forests) is likely responsible for the increase in the tree squirrel component at these latitudes. Overall, our study confirmed assumptions of the latitudinal gradient theory.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".