Mapping Distribution in Oklahoma and Raising Awareness: Purple Loosestrife (Lythrum salicaria), Multiflora Rose (Rosa multiflora), and Japanese Honeysuckle (Lonicera japonica)
Bibliographic record
Abstract
This paper includes updated Oklahoma distribution maps and informational fact sheets for purple loosestrife, multiflora rose, and Japanese honeysuckle to promote awareness of invasive plant issues.The current information on the Internet contains discrepancies concerning the county-level distribution data of these three invasive plants in Oklahoma.To gain a more accurate dataset, the authors created a survey and sent it to Oklahoma State University Extension Educators, Master Gardeners, Oklahoma Association of Conservation Districts, and other knowledgeable, credible parties across Oklahoma.Once survey data were compiled, 3 distribution maps were created and 6 unique fact sheets were produced with the updated information.From the 22 survey responses, 7 new county records were documented and mapped.Two new sightings were documented for purple loosestrife in Canadian County and Rogers County; 4 new sightings were documented for multiflora rose in the counties of Atoka, Johnston, Payne, and Pushmataha; and 1 new sighting was documented for Japanese honeysuckle in Garfield County.The findings in this research detail the need for updated distribution maps and increased education to prevent the spread of problem species and provide the public with methods of eradication.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".