Spatial analysis of two aquatic invaders in Adirondack Lakes: a modelling approach for environmental management
Bibliographic record
Abstract
The global expansion of humans has stressed the natural world, removed boundaries between continents and habitats and exposed natural areas to invasive species. These cause billions of dollars of damage yet there are limited funds given for their management. Predictive tools can be used to develop pro-active strategies for managing invasive species and this study developed such a tool. Publicly available data were used to build predictive models for the presence of two invasive species, curly-leaf pondweed (Potamogeton crispus) and Eurasian watermilfoil (Myriophyllum spicatum) within the Adirondack Park (New York State). Predictors were identified through: bivariate analysis to test the variables; ordinary least squares regression to build predictive models and logistic regression to validate those models; geographically weighted logistic regression to evaluate local impacts. Models were ranked by Aikake information criterion minimization and evaluated with McFadden’s rho-squared, standard coefficients and variance inflation factors. The top five models for each invasive species established seven predictors for curly-leaf pondweed and nine predictors for Eurasian watermilfoil. Geographically weighted regression, a local analysis, was found to be a definite improvement over the global analysis for watermilfoil but not for pondweed. Two predictors (lake elevation and distance to Interstate-87) were significant in all the top models for both species. The identified predictors provided a group of characteristics that could be used to identify vulnerable lakes and prioritize management strategies. Even though these findings were specific to the Adirondack Park, this approach could be applied to other invasive species or other areas to help in the decision-making process for management.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".