Mapping the purple menace: spatiotemporal distribution of purple loosestrife (Lythrum salicaria) along roadsides in northern New York State
Bibliographic record
Abstract
Purple loosestrife (Lythrum salicaria L.) is an invasive, herbaceous plant, frequently found in wetlands, creating monoculture stands, resulting in intensive management strategies in central New York, Ontario, and Quebec. The goal of this study was to identify the extent of infestations and to investigate factors that promote the spread of purple loosestrife. We attempted to answer several questions regarding level of infestation, connection to mowing, and influence of culverts. During flowering season in July and August, 2017-2019, we mapped infestations along 150 km (93 miles) of state highway between the Adirondack Park and the St. Lawrence River using the ESRI Collector app. The results of our preliminary analysis revealed significant increase in the number of plants (P < 0.001). In addition, a linear correlation analysis demonstrated a higher loosestrife density with an increase in plant species richness and a decrease in the distance to the closest infestation and wetland (P < 0.001 each). We found no statistical evidence that mowing promotes the spread of loosestrife. As expected, there were more individual infestations in highway ditches, but larger and denser infestations in wetlands (P = 0.003 in 2019). Culverts enable purple loosestrife to spread underneath highways and should be managed to prevent spread.
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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".