PREDICTING OCCURRENCES AND IMPACTS OF SMALLMOUTH BASS INTRODUCTIONS IN NORTH TEMPERATE LAKES
Bibliographic record
Abstract
Smallmouth bass and other warmwater littoral piscivores are presently expanding their geographic range northward into lakes across southern Canada. Smallmouth bass introduction can dramatically reduce minnow abundances, causing native lake trout to shift to low quality, invertebrate-based diets. Here we develop models to predict future occurrences and impacts of Smallmouth bass in central Ontario, with the goal of identifying "vulnerable" lakes in order to better guide prevention efforts. Using local and regional environmental variables for 3046 central Ontario lakes, an artificial neural network was used to predict lakes that are likely to be invaded by bass. Smallmouth bass can significantly influence the occurrence and abundance of small-bodied fishes (mainly minnows), and stable isotope analysis of food webs in 18 lakes revealed that lake trout are buffered from impacts of bass on minnows in lakes containing pelagic prey fishes. In the absence of pelagic prey fishes, the trophic niche of lake trout depends on the presence of bass; lake trout feed primarily on zooplankton in the presence of bass, and minnows in the absence of bass. Of the 3046 lakes, the 788 lake trout lakes in central Ontario were classified according to their vulnerability to bass invasion based on the predictability of bass occurrence and their subsequent impacts, and mapped in a Geographic Information System (GIS). Only 48 lake trout lakes (6%) were classified as "high vulnerability" - predicted to be invaded and impacted by bass. Another 301 lakes had a sensitive food web structure but were not predicted to support a bass population. Based on this information, efforts to prevent further impacts can be optimized by focusing on this vulnerable subset of lakes.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".