Aggregated occurrence records of invasive European frog-bit (Hydrocharis morsus-ranae L.) across North America
Bibliographic record
Abstract
European frog-bit (Hydrocharis morsus-ranae L.) is an invasive aquatic plant of growing concern in the Laurentian Great Lakes region. This species has the potential to continue to spread throughout North America and may threaten coastal and inland ecosystems and native species. Central to management of European frog-bit is an understanding of the historic and current distribution, which can inform efforts to predict future spread and establishment, determine habitat suitability and the factors that drive invasion, and identify high-priority areas for targeted monitoring and management. We created an aggregated dataset of European frog-bit occurrence records from 1932 to 2021 across the invasive range of North America. A total of 23,985 records were initially obtained from eight observation-based data providers and forty specimen-based data providers. Two datasets were mobilized for this project, including 2,874 records from a targeted university research effort and 150 unpublished specimens from Agriculture and Agri-Food Canada National Collection of Vascular Plants. After data cleaning, standardization, and validation, this dataset contains 12,037 unique presence and absence records spanning thirteen U.S. states and two Canadian provinces.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".