Potential role of the seed bank in spreading invasive plants in a tundra-edge environment
Bibliographic record
Abstract
Although relatively infrequent, invasions by non-native plants at high latitudes are increasingly recognized as an emerging problem. Churchill, MB, Canada is unusual as over 100 non-native species have been found in human-disturbed areas, making it an outlier amongst subarctic habitats. Although these non-native species have persisted almost exclusively within town, some occur in isolated locations throughout the local road network. Most of these non-native species have been observed in areas with a history of soil movement (e.g., for construction or road repairs), suggesting that they have been moved within the soil, likely either germinating from seeds or growing clonally from root and rhizome fragments. Using a greenhouse experiment, we found evidence that soils from human-disturbed sites can contain a substantial non-native seed bank. In particular, we grew a significantly higher number of non-native seedlings from translocated soils compared with uninvaded soils. These germinated non-native species are native to Europe but have widely invaded temperate regions. This study provides the first direct evidence that movement of seed-contaminated soil is a significant source of local non-native species spread. Future warming in Arctic regions may increase seed production of these species, leading to increased spread and persistence via contaminated soil.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".