Geographic structure of genetic variation in the widespread woodland grass<i>Milium effusum</i>L. A comparison between two regions with contrasting history and geomorphology
Bibliographic record
Abstract
Allozyme variation in the forest grass Milium effusum L. was studied in 21-23 populations within each of two equally sized densely sampled areas in northern and southern Sweden. In addition, 25 populations from other parts of Eurasia were studied for comparison. The structure of variation was analysed with both diversity statistics and measures based on allelic richness at a standardised sample size. The species was found to be highly variable, but no clear geographic patterns in the distribution of alleles or in overall genetic differentiation were found, either within the two regions or within the whole sample. Thus, no inferences about the direction of postglacial migration could be made. Obviously, migration and gene flow must have taken place in a manner capable of randomising the distribution of alleles. However, there were clear differences in levels and structuring of the variation between the two regions. Levels of variation, both in terms of genetic diversity and allelic richness, were lower in northern Sweden as compared with southern Sweden. In contrast, different measures of geographic structure all showed higher levels of population differentiation in the northern region. This is interpreted as due to different geomorphological conditions in the two regions, creating a relatively continuous habitat and gene flow in the southern region as compared with the northern region where the species, although common, is confined to narrow and mutually isolated corridors in the landscape.
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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 | 0.001 |
| 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".