Effects of an invasive bivalve (<i>Dreissena polymorpha</i>) on fish in the Hudson River estuary
Bibliographic record
Abstract
Despite predictions that the zebra mussel (Dreissena polymorpha) invasion of North America would damage fisheries, analyses of actual effects on fish have been few and equivocal. We analyze 26 years of data on fish populations in the Hudson River to quantify changes associated with the zebra mussel invasion. Based on our measurements of changes in the lower food web, we predicted that populations of open-water fish species (e.g., Alosa spp.) would suffer and populations of littoral fish species (e.g., Centrarchidae) would prosper from the zebra mussel invasion. We found that the median decrease in abundance of open-water species was 28%, whereas the median increase in abundance of littoral species was 97%. Populations of open-water species shifted downriver away from the zebra mussel population, whereas those of littoral species shifted upriver. Median apparent growth rates fell by 17% among open-water species and rose by 12% in the single littoral species studied. Many of the observed changes were large and involved species of commercial or recreational importance (e.g., American shad (Alosa sapidissima), black basses (Micropterus spp.)). The influence of zebra mussels on fish should vary widely across ecosystems as a function of system morphology, factors that limit primary production, and diets of the fish species.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".