Lesser Celandine (<i>Ranunculus ficaria</i>): A Threat to Woodland Habitats in the Northern United States and Southern Canada
Bibliographic record
Abstract
Abstract Lesser celandine (Ranunculus ficaria) is an ephemeral perennial introduced to North America from Europe for ornamental use. This species is becoming widespread in shady, moist woodlands and lawns in the northern United States and southern Canada. Despite its invasive attributes, lesser celandine continues to be marketed by the nursery sector. A hardy and easy plant to grow, lesser celandine has a dense compact growth form that makes it ideal for border plantings and showy flowers that make it a desirable garden species for early spring color. The species easily escapes cultivation, colonizing primarily moist woodland habitats. Once established, lesser celandine creates a monoculture and is purported to displace native ephemeral species. Several factors make control of lesser celandine difficult, including a large root structure, which facilitates clonal reproduction and spread. The ephemerality of lesser celandine results in a relatively brief window in which to apply control measures. Land managers need to be aware of this common garden species as a potentially invasive weed and need to be able to differentiate it from the similar native marsh marigold. Effective management strategies are needed to protect native woodland understory species and biodiversity in natural and seminatural areas of affected regions.
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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 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".