SUBSPECIFIC DIFFERENTIATION AND CONSERVATION OF SONG SPARROWS (MELOSPIZA MELODIA) IN THE SAN FRANCISCO BAY REGION INFERRED BY MICROSATELLITE LOCI ANALYSIS
Bibliographic record
Abstract
We examined genetic population structure of five putative subspecies of Song Sparrows (Melospiza melodia) in the San Francisco Bay region (M. m. samuelis, M. m. maxillaris, M. m. pusillula, M. m. gouldii, and M. m. heermanni) at nine microsatellite loci to assist the development of Song Sparrow conservation and management strategies. We sampled nine populations from five putative subspecies and found low estimates of differentiation between populations within subspecies and between. Despite low estimates of divergence, genetic structure at the subspecies level was indicated by the larger amount of variance accounted for by subspecies than populations. We propose that a management unit encompassing the range of M. m. pusillula be given priority for conservation on the basis of the extent of genetic divergence shown by Cavalli-Sforza and Edward's chord distance, and the topology of an unweighted pair group cluster analysis supported by 100% of bootstrap replicates across loci. Although M. m. samuelis and M. m. maxillaris appear undifferentiated from M. m. heermanni, it remains possible that adaptive differences between those types were not identified with neutral loci.
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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 | 0.000 |
| 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) | 0.000 | 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".