From the Source to the Outlet: understanding the Distribution of Invasive Knotweeds along a North American River
Bibliographic record
Abstract
Abstract Understanding the drivers of exotic plant invasions along waterways is crucial for helping environmental managers devise effective control strategies. We combined a field survey, molecular data and a logistic regression model to further our understanding of the spatial distribution of Japanese (Fallopia japonica) and Bohemian (Fallopia × bohemica) knotweeds along the entire course (185 km) of a river located in Québec (Canada). Both knotweeds were abundant along the river, but each had a distinct spatial distribution pattern. Only one genotype for each knotweed species or hybrid was found, suggesting that the individuals established along the Chaudière River resulted from the propagation of rhizome or stem fragments. The distance from the nearest town or village was the only explanatory variable significantly correlated to the spatial distribution of knotweeds. However, spatial autoregressive coefficients were significant, indicating that knotweeds were more likely to occur close to other knotweeds. In summary, the invasion was probably initiated by the introduction, in riverside towns and villages, of a few individuals of the same genotype. The clones then spread vegetatively, probably during spring floods. The rhizome and stem fragments spread over short distances, dispersing downstream from urban centres. The introduction of just two knotweed genotypes along the Chaudière River was sufficient to initiate a massive riverside colonization, as few riparian vegetation types were apparently able to resist knotweed invasion. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".