Mapping the Purple Menace: Spatiotemporal Distribution of Purple Loosestrife (Lythrum Salicaria) Along Roadsides in Northern New York State
Bibliographic record
Abstract
Abstract Purple loosestrife (Lythrum salicaria L.) is an invasive herbaceous plant, frequently found in wetlands and along roadsides throughout northern New York State. Its propensity to create monoculture stands in wetlands has resulted in intensive management in parts of its range, including central New York and Ontario and Quebec. The goal of this study was identifying the extent of infestations and to determine factors that promote the spread of purple loosestrife in order to determine best land use practices to reduce the spread of this highly invasive wetland plant." We attempted to answer several questions: What is the level of infestation along roadsides? Does mowing contribute to the spread? How do culverts under roadways influence the spread? Between mid-July and mid-August in 2017–2019, we mapped all infestations along 150 km (93 miles) of state highway north of the Adirondack Park and south of the St. Lawrence River using the Environmental Systems Research Institute (ESRI) Collector app. We mapped the size and locations of infestations, in addition to collecting information on the plants within 1 m, recording over 100 additional species. The results of our preliminary analysis revealed significant increase in the number of plants over the study time (P < 0.001), including several hundred more infestations than had previously been recorded. We did not find any evidence that mowing promotes the spread of loosestrife. There were many more individual infestations in ditches along highways, but much larger and denser infestations in wetlands (P = 0.003 in 2019). We observed that culverts, drainage conduits under roadways, frequently had purple loosestrife on both sides of the road. We also found that culverts appear to serve as corridors that promote the spread of purple loosestrife from one side of the road to the other. We recommend working with the local landowners and NYS Department of Transportation to contain the spread and continue to propagate and spread the biological control.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".