Evidence from sequence-tagged-site markers of a recent progenitor-derivative species pair in conifers
Bibliographic record
Abstract
Black spruce (Picea mariana [B.S.P.] Mill.) and red spruce (Picea rubens Sarg.) are two conifer species known to hybridize naturally in northeastern North America. We hypothesized that there is a progenitor-derivative relationship between these two taxa and conducted a genetic investigation by using sequence-tagged-site markers of expressed genes. Based on the 26 sequence-tagged-site loci assayed in this study, the unbiased genetic identity between the two taxa was quite high with a value of 0.920. The mean number of polymorphic loci, the mean number of alleles per polymorphic locus, and the average observed heterozygosity were lower in red spruce (P = 35%, A(P) = 2.1, H(o) = 0.069) than in black spruce (P = 54%, A(P) = 2.9, H(o) = 0.103). No unique alleles were found in red spruce, and the observed patterns of allele distribution indicated that the genetic diversity of red spruce was essentially a subset of that found in black spruce. When considered in combination with ecological evidence and simulation results, these observations clearly support the existence of a progenitor-derivative relationship and suggest that the reduced level of genetic diversity in red spruce may result from allopatric speciation through glaciation-induced isolation of a preexisting black spruce population during the Pleistocene era. Our observations signal a need for a thorough reexamination of several conifer species complexes in which natural hybridization is known to occur.
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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 | 0.001 |
| 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".