Increasing broad-spectrum aquatic invasive species early detection program efficiency through biased site selection and gear allocation
Bibliographic record
Abstract
Abstract Invasive species cause severe environmental and economic damage throughout the globe. Aside from preventing their introduction, early detection of newly introduced species is the most successful method to prevent their establishment, spread, and eventual negative impacts. Broad-spectrum monitoring for the early detection of novel non-native species is oftentimes heavily burdened by the inherent difficulty in maximizing the detection probabilities of numerous high priority species simultaneously with only finite resources. We attempted to increase the efficiency of broad-spectrum monitoring in four locations across Lake Erie (USA)—the Detroit River, Maumee Bay, Sandusky Bay, and Cleveland—by targeting our site and gear selections to maximize overall species richness and detection rates of rare and non-native species, with the results compared to a random sampling design. Overall species richness was significantly higher in all four locations, while non-native species detection rates were significantly higher in every location except for Cleveland. Detection rates of rare species was significantly higher in Maumee Bay only. Our results indicate this selective sampling design is more likely to detect a newly introduced non-native species than a random sampling design and are in support of the established literature for broad-spectrum monitoring for novel aquatic invasive species.
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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 | 0.001 | 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) | 0.000 | 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".