Effect of introduced piscivores on native minnow communities in Adirondack lakes
Bibliographic record
Abstract
We compared the minnow assemblages of Adirondack lakes with top piscivores with those of lakes having similar physiochemical and biotic characteristics but no top piscivores using a subset of data collected from 1984 to 87 by the Adirondack Lake Survey Corporation. Native minnow richness in lakes with top piscivores was about one third that of lakes without piscivores, with piscivore assemblages dominated by introduced species such as northern pike (Esox lucius), largemouth bass (Micropterus salmoides), and smallmouth bass (Micropterus dolomieu). There was strong evidence that at least four minnow species, including creek chub (Semotilus atromaculatus), northern redbelly dace (Phoxinus eos), blacknose dace (Rhinichthys atratulus), and common shiner (Luxilus cornutus), were less likely to occur in lakes with piscivores; for a fifth species (pearl dace (Margariscus margarita)), the evidence is suggestive but not as strong. Of 13 minnow species, only for two nonnative species (bluntnose minnow (Pimephales notatus) and golden shiner (Notemigonus crysoleucas)) was there strong evidence that their occurrence was unaffected by the presence of introduced piscivores. These results add to the growing body of evidence that the introduction of top piscivores to small temperate lakes puts native minnow communities at high extinction risk.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".