Giant hogweed at its northern distribution limit in North America: Experiments for a better understanding of its dispersal dynamics along rivers
Bibliographic record
Abstract
Abstract We studied the reproduction and dispersal of giant hogweed (Heracleum mantegazzianum) at its northern distribution limit in North America (Québec, 2014–2016) to better understand the invasion dynamics of the species along rivers. Seeds were collected from a riparian population to conduct germination, floatation, and dispersal experiments. Data were analysed in comparison with a real invasion case that was initiated about 10 years ago along a river system. In Québec, giant hogweed individuals produce on average 14,000 to 16,000 seeds with a germination rate of 75–85%. Seeds with endosperm that fall in water likely sink within 5 hr. Along a small brook, most disperse over short distances (<40 m) in summer, although some can travel 100–300 m. These data suggest that late‐summer or early‐fall water dispersal of seeds would not explain the magnitude and rapidity of the invasion patterns observed along streams. We suggest that late‐fall and, especially, spring floods are the most efficient dispersal vectors for giant hogweed seeds and are likely responsible for the establishment of populations kilometres downstream from introduction points along river systems. The spread of giant hogweed would consequently be less influenced in the near future by a rise in temperature than by a change in the magnitude or timing of flood events.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".