Dreissena distribution in commercial waterways of the U.S.: using failed invasions to identify limiting factors
Bibliographic record
Abstract
Preventing the introduction of an invasive species and managing the ecological and economic effects of successful invasions often rely on accurate predictions of the potential distribution of the species in its new habitat. Instances of failed invasion can highlight factors other than dispersal that limit range expansion in an invasive species. The commercially available waterways (CAWs) of the U.S. are a network of interconnected rivers that support a regular traffic of commercial and maintenance vessels. As such, they are at extremely high risk of infestation from Dreissena, yet not all the rivers are infested. We used traffic, water chemistry, and impoundment characteristics of the rivers in this system to develop a set of logistic-regression models to predict the occurrence of Dreissena. Four single variable models correctly classified 20 out of 24 (83%) river systems. The best two-variable models had only one misclassified river, for an overall accuracy of 96%. Among rivers to which Dreissena has successfully dispersed, permanent populations are most likely to establish in systems that have moderate ionic strength and that also have some impoundment areas to increase long-term population persistence. We use this hierarchy of factors to extend predictions to four systems that currently lack the high dispersal intensity of the CAWs.
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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 | 0.001 | 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) | 0.001 | 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 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".