Effects of Caffeine on Fish Learning
Bibliographic record
Abstract
Pollution is an increasing threat to health and biodiversity, especially chemical pollution in the air, land, and water. One such example is caffeine, which is a main active ingredient in coffee and is ingested by humans worldwide for its stimulant effects and cultural significance. This widespread caffeine ingestion coupled with incomplete removal during wastewater treatment results in high concentrations of caffeine in the environment. Aquatic organisms living in waterways receiving wastewater effluent are often exposed to caffeine continuously. Given this long-term and widespread exposure, caffeine is an emerging contaminant of concern. However, most research investigating the effects of caffeine on aquatic organisms use caffeine doses that are much higher and caffeine exposure durations that are much shorter than those found in the environment. Also, most caffeine exposure studies also rely on relatively simple behavioural endpoints and make use of neotropical species. In contrast, I exposed fathead minnow (Pimephales promelas), a common freshwater fish in North America, to environmentally relevant concentrations of caffeine (0 ng/L; 1,000 ng/L; 10,000 ng/l) for 35 days. Caffeine exposure did not affect morphology (e.g., length, mass, growth) or metabolism (maximum metabolic rate, resting metabolic rate, and aerobic scope), but decreased their hepatosomatic index (liver investment). While caffeine did not affect the number of trials taken to associative or reversal learn, or the latency of fish to avoid an aversive trawl, three weeks of exposure to low caffeine concentrations may have decreased anxiety. Taken together our results suggest that future studies perhaps with different endpoints are needed clarify our understanding of how caffeine influences metabolism, anxiety, and learning. Overall, our results provide evidence that complex behavioural endpoints such as aversive learning can be used in ecotoxicological studies.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".