Geo-referenced population-specific microsatellite data across American continents, the MacroPopGen Database
Bibliographic record
Abstract
Abstract Population genetic data from nuclear DNA has yet to be synthesized to allow broad scale comparisons of intraspecific diversity versus species diversity. The MacroPopGen database collates and geo-references vertebrate population genetic data across the Americas from 1,308 nuclear microsatellite DNA studies, 897 species, and 9,090 genetically distinct populations where genetic differentiation (FST) was measured. Caribbean populations were particularly distinguished from North, Central, and South American populations, in having higher differentiation (FST = 0.12 vs. 0.07–0.09) and lower mean numbers of alleles (MNA = 4.11 vs. 4.84–5.54). While mammalian populations had lower MNA (4.86) than anadromous fish, reptiles, amphibians, freshwater fish, and birds (5.34–7.81), mean heterozygosity was largely similar across groups (0.57–0.63). Mean FST was consistently lowest in anadromous fishes (0.06) and birds (0.05) relative to all other groups (0.09–0.11). Significant differences in Family/Genera variance among continental regions or taxonomic groups were also observed. MacroPopGen can be used in many future applications including latitudinal analyses, spatial analyses (e.g. central-margin), taxonomic comparisons, regional assessments of anthropogenic impacts on biodiversity, and conservation of wild populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".