Russian-olive (<i>Elaeagnus angustifolia</i>) Biology and Ecology and its Potential to Invade Northern North American Riparian Ecosystems
Bibliographic record
Abstract
Abstract Russian-olive is a small tree or large multistemmed shrub that was introduced to Canada and the United States from Eurasia in the early 1900s. It was provisioned in large numbers during the last century to prairie farmers as a shelterbelt plant and remains a popular and widely available ornamental. Now invasive within some riparian ecosystems in the western United States, Russian-olive has been declared noxious in the states of Colorado and New Mexico. With traits including high shade tolerance and a symbiotic association with nitrogen-fixing bacteria, Russian-olive has the potential to dominate riparian vegetation and thus radically transform riparian ecosystems. Especially alarming is its capacity to influence nutrient dynamics within aquatic food webs. Our objective is to draw attention to Russian-olive as a potential threat to riparian ecosystems within Canada, especially in the southwest, where invasion is becoming commonplace. We review what is known about its biology and about the threats it poses to native organisms and ecosystems, and we summarize management and control efforts that are currently underway. We conclude by proposing a research agenda aimed at clarifying whether and how Russian-olive poses a threat to riparian ecosystems within western Canada.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".