HIERARCHICAL ANALYSIS OF GENETIC POPULATION STRUCTURE IN THE NOISY MINER USING DNA MICROSATELLITE MARKERS
Bibliographic record
Abstract
The Noisy Miner (Manorina melanocephala) is a common, cooperatively breeding meliphagid of eastern and southeastern Australia. We used DNA microsatellites to examine the apportionment of genetic diversity in Noisy Miners at disparate geographic scales to generate baseline information about the population genetic structure of this species. We examined five loci for 79 individuals from four populations. We found small amounts of genetic differentiation between paired populations separated by 30–40 km, and more substantial and significant differentiation between populations separated by more than 1000 km. This result implies substantial gene flow on local scales but much less at regional levels, probably reflecting short dispersal distances and isolation by distance. Análisis Jerárquico de la Estructura Genética Poblacional de Manorina melanocephala Utilizando Marcadores de ADN Microsatelital Resumen. Manorina melanocephala es un melifágido común del sur-este de Australia que presenta cría cooperativa. Utilizamos ADN microsatelital para examinar la distribución de la diversidad genética de M. melanocephala a diferentes escalas geográficas para generar información básica sobre la estructura genética poblacional de esta especie. Examinamos 5 loci en 79 individuos pertenecientes a cuatro poblaciones. Encontramos una baja diferenciación genética entre pares de poblaciones separadas por 30–40 km, y una diferenciación más substancial y significativa entre poblaciones separadas por más de 1000 km. Estos resultados sugieren un alto grado de flujo génico a escala local, el cual es mucho menor a nivel regional, lo que probablemente refleja distancias de dispersión cortas y aislamiento por distancia.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".