Effects of elevated temperature and microplastic exposure on growth and predatory performance of a freshwater fish
Bibliographic record
Abstract
Abstract Freshwater ecosystems are increasingly exposed to co‐occurring anthropogenic stressors that can alter food web interactions and organismal life histories. We examined the individual and combined effects of climate warming and microplastic pollution on the growth rate and predatory performance of an invasive freshwater fish, the round goby (Neogobius melanostomus). In temperature‐controlled chambers, we exposed 160 juvenile gobies to one of six scenarios over 37 d, combining three environmentally relevant concentrations of microplastics (63–75 μm polyethylene microbeads) with two temperature regimes representing contemporary (18°C) and projected mean summer maxima (26°C) in their current range in the Great Lakes–St. Lawrence River basin. Exposure to elevated temperature reduced the growth and predatory performance of round gobies. Their decline in predatory performance was greatest at the highest microplastic concentration, regardless of temperature. The effects of environmentally relevant microplastic concentrations on the growth and performance of gobies were weaker than the effects of thermal stress. Given that the round goby is an abundant and widely distributed bottom‐dwelling fish in nearshore areas of the Great Lakes–St. Lawrence River basin, its responses to these co‐occurring stressors could have cascading effects on food webs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".