A PCR-RFLP method to detect hybridization between the invasive Eurasian watermilfoil (<i>Myriophyllum spicatum</i>) and the native northern watermilfoil (<i>Myriophyllum sibiricum</i>), and its application in Ontario lakes
Bibliographic record
Abstract
The discovery of hybridization between the invasive Eurasian watermilfoil (Myriophyllum spicatum L.) and native northern watermilfoil (Myriophyllum sibiricum Kom.) has generated interest in establishing the hybrid’s distribution and invasiveness. Identification of hybrid M. spicatum × M. sibiricum requires molecular genetic analysis, however, as the hybrid’s morphology overlaps with both parent species. Using plants collected from 10 lakes in Ontario, Canada, we compared a previous method of identification (sequencing the nuclear ITS region) with a simpler screening method (PCR-RFLP of the ITS region). Both methods agreed on the identification of hybrid M. spicatum × M. sibiricum and both parent species, supporting the suitability of PCR-RFLP to screen for the hybrid. Four of 29 samples were identified as hybrid M. spicatum × M. sibiricum, which were all found in three adjacent lakes associated with the Rideau Canal Waterway. The PCR-RFLP method should enable greater sampling effort to screen for hybrid M. spicatum × M. sibiricum and establish its geographic distribution across connected waterways.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".