Genetic composition and morphological variation among populations of the invasive grass,<i>Phalaris arundinacea</i>
Bibliographic record
Abstract
Species that become invasive after being introduced into a new range often experience genetic bottlenecks and strong selection to adapt to their new environment. We looked for evidence of such processes in unmanaged populations of invasive reed canary grass (Phalaris arundinacea L.). This grass species is planted as a forage crop in North America but has also invaded wetland areas. We compared isozyme variation in pasture and wetland populations of this species. We did not find any indication of a genetic bottleneck: wetland populations comprised as much diversity as pasture populations and both had as much diversity as the two cultivated varieties of reed canary grass that we sampled. We also cultivated plants from wetland and pasture populations and estimated genetic variance for several morphological traits. We did not find any significant differentiation to suggest differential selection between populations from the two habitats. In fact, we found the highest amount of genetic diversity, both isozymic and quantitative, within populations. We also found strong evidence that reed canary grass reproduces primarily clonally. The implications of these observations in terms of the origin of invasive populations of reed canary grass are discussed.Key words: invasive species, Phalaris arundinacea, reed canary grass.
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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".