A simple method to genetically differentiate invasive F <sub>1</sub> <i>Typha</i> hybrids ( <i>T</i> . × <i>glauca</i> ) and advanced-generation/backcrossed hybrids from parent species ( <i>T. latifolia</i> and <i>T. angustifolia</i> ) in eastern Canada and northeastern USA
Bibliographic record
Abstract
Abstract A widespread Typha (cattail) hybrid zone in southeastern Canada and northeastern USA comprises T. latifolia, T. angustifolia , and their hybrid T . × glauca . In this region the hybrid is considered invasive because of its detrimental impacts on wetlands and biodiversity. Researchers and managers are increasingly interested in tracking and controlling the spread of the invasive hybrid, but this can be hampered by challenges in differentiating taxa: while the parent species and F1 hybrids can often be identified from morphological characteristics, advanced-generation and backcrossed hybrids can be common, and these have obscured morphological distinctions among taxa. Here we present five species-specific genetic markers that differentiate T. latifolia, T. angustifolia , F 1 hybrids, and advanced-generation/backcrossed hybrids. Unlike the pre-existing species-specific microsatellite markers, these markers require only PCR or PCR followed by a restriction enzyme digest, and can be visualized on an agarose gel. As a result, they require less specialized equipment than existing species-specific markers, and should therefore be useful for a larger group of practitioners. Future use of these markers could include monitoring range expansions, assessing the outcomes of restoration programs, and avoiding the unnecessary elimination of native Typha .
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".