Modelling spread of the invasive macrophyte <i>Cabomba caroliniana</i>
Bibliographic record
Abstract
Summary 1. Predicting spread of non‐indigenous species requires an understanding of where propagules are being transported, and whether these propagules can survive in the novel habitat and successfully integrate into the recipient community. In this study, we model potential spread of invading Cabomba caroliniana in Ontario, Canada, using a combination of passive and active dispersal models coupled with an environmental suitability model, thereby considering the first two stages of the invasion process. 2. Measures of propagule pressure incorporated both human‐mediated dispersal via trailered boats, and advective flow from invaded to non‐invaded systems, while habitat suitability was forecasted by combining native and global data sets and using boosted regression trees. 3. Risk of invasion differed depending on the combination of approaches used and the time period considered. Three lakes appear to be at greatest risk owing to a combination of high boater and water movement from invaded sources, and high environmental suitability. The best predictors of lake suitability were pH, mean lake temperature and dissolved calcium concentration. Hundreds of lakes in Ontario may be suitable for establishment of Cabomba , highlighting the need for vector management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.013 | 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 teacher head, 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".