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 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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".