Invasive goldfish ( <i>Carassius auratus</i> ) maintain aerobic scope across acute warm water temperatures
Bibliographic record
Abstract
Goldfish (Carassius auratus) were first introduced to the Laurentian Great Lakes when it was first introduced into Lake Ontario in the 1800s. In the past 15 years, there have been dramatic increases in both goldfish abundance and geographic spread across North America, including the Great Lakes, raising concerns about its potential for negative impacts on aquatic ecosystems. Climate studies suggest that habitat ranges suitable for goldfish will continue to expand in the future as water temperatures rise towards its thermal optima. We explore whether warmer temperatures are physiologically suitable for a population of wild, invasive goldfish (Hamilton, ON, Canada) by testing aerobic scope (AS) at current (26°C) and predicted (30°C) peak summer water temperatures. Goldfish were first acclimated to a common-garden average summer pond temperature (22°C), then their AS was estimated by calculating the difference between routine and maximum metabolic rates (RMR and MMR) at the two test temperatures. Our results demonstrate that wild goldfish sustain their AS through increases in both RMR and MMR from 26°C to 30°C (mass-standardized means of 1.07 versus 1.17 mgO2h-1 at 26°C versus 30°C, respectively). This ability to sustain aerobic energy budget at high peak water temperatures could offer physiological benefits to this invasive population in a warming climate.
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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".