Subalpine fir microsatellite variation reveals the complex relationship between var. <i>lasiocarpa</i> and var. <i>bifolia</i>
Bibliographic record
Abstract
Subalpine fir, Abies lasiocarpa, occurs throughout western North America, often in forest–tundra parkland. To resolve the presence of varieties in this species, we surveyed microsatellite genetic markers in 11 populations containing three putative varieties of Abies lasiocarpa: (1) var. lasiocarpa, (2) var. bifolia, and (3) var. arizonica. We tested primers from related taxa, and 13 of the best primer pairs were used for assays. Within populations, both heterozygosity and allelic richness were approximately 10% lower in var. lasiocarpa. The STRUCTURE procedure struggled to assign populations to groups correctly; at K = 3, individuals were assigned to their putative varieties with approximately 70% accuracy. Regardless, both lasiocarpa and bifolia were correctly assigned more than expected by chance, indicating that these taxa are distinct. A dendrogram of genetic distances showed var. arizonica to exhibit higher evolutionary distance from the other two varieties and serves as an outgroup. The dendrogram also showed a nesting of var. bifolia clades within var. lasiocarpa, indicating a complex relationship between var. lasiocarpa and var. bifolia. Comparisons among the STRUCTURE population assignments for K = 2, K = 3, and K = 4 identified populations with cryptic admixture and indicate a “ lasiocarpa–bifolia” subspecies complex that warrants further study.
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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) | 0.001 | 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".