The potential role of public gardens as sentinels of plant invasion
Bibliographic record
Abstract
Abstract Public gardens can help prevent detrimental effects of plant invasions by collecting and sharing data on taxa spreading from cultivation early in the invasion process, thereby acting as sentinels of plant invasion. Existing initiatives have called for public gardens to adopt measures preventing plant invasion, but it is unclear what actions individual gardens are implementing, as there is no formal mechanism for communicating their progress. This study used internal lists of escaping taxa from seven public gardens in the Midwestern United States and Canada to demonstrate how public gardens can collectively contribute data that is critical to assessing potential invasiveness. It also reveals methodological differences in how gardens develop their lists of escaping plants, leading to recommendations for standardization. Data pooled across gardens yielded 769 species spreading from cultivation at one or more gardens. Eight woody species were listed by all gardens despite not consistently being recognized as invasive by states and provinces containing the gardens; some species recorded by multiple gardens did not appear on any invasive lists. While it may be premature to call taxa escaping from cultivation at a few public gardens “invasive” or even “potentially invasive”, these plants should be monitored and evaluated with this information shared to facilitate stronger conclusions about risk. Thus, public gardens have a unique expertise in assisting invasive plant efforts as sentinels, particularly if challenges related to methodological inconsistencies and data sharing are suitably addressed, which is herein recommended through the adoption of a set of standardized guidelines.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".