Data from sentinel public gardens are useful indicators of potential plant invasion
Bibliographic record
Abstract
Abstract Invasive plants negatively impact natural areas and impose huge costs associated with control and management. A new approach to significantly reduce these effects is to identify species in the earliest stages of spread, using data collected by public gardens across North America. Known as Public Gardens as Sentinels against Invasive Plants (PGSIP), this network includes multiple gardens, each contributing reports of these problematic species to a shared database using standardized guidelines. We examined this dataset to identify newly spreading species being noticed within gardens within different regions and determined whether they have been reported as state-listed/regulated/noxious outside gardens. As of November 2024, 53 PGSIP gardens in 28 U.S. states and Canadian provinces had submitted 996 reports, consisting of 597 unique species. The most commonly listed species were Amur corktree ( Phellodendron amurense Rupr.), burning bush [ Euonymus alatus (Thunb.) Siebold], and wintercreeper [ Euonymus fortunei (Turcz.) Hand.-Maz.]. Other less frequently listed species included golden rain tree ( Koelreuteria paniculata Laxm.), Norway maple ( Acer platanoides L.), and castor aralia [ Kalopanax septemlobus (Thunb.) Koidz.]. Of the 597 species, 36% were not listed by any state or province; gardens also had several species on watchlists, including Japanese tree lilac [ Syringa reticulata (Blume) H. Hara] and Siberian squill ( Scilla siberica Andrews). Our results demonstrate the utility of the approach and value of the database. This information can now inform the efforts of land managers, invasion biologists, the horticultural industry, and agencies tasked with invasive plant monitoring and assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".