Unintended Consequences of Aquatic Enrichment in Experimental Biology
Bibliographic record
Abstract
Enrichment in aquatic animal studies is important for promoting welfare and maintaining animal health and can be categorized by physical, sensory, social, occupational, and dietary enrichment. However, the risk of potential chemical leaching associated with physical enrichment items has been largely overlooked (i.e., artificial plants or shelter). Most enrichment items lack information on their chemical composition and have not undergone testing for plastic or metal leachates that can alter water chemistry and impair animal physiology. In fish and invertebrate research, these leachates have the potential to modify the health of aquatic animals or their reproductive processes. Moreover, in toxicology research, altered chemical exposure concentrations and interactive effects with leachates could invalidate toxicity assays and lead to misleading results. We identify key contaminants associated with common enrichment items and highlight the substantial lack of empirical research focusing on the confounding factors associated with aquatic enrichment. We explore the mechanisms through which relevant leachates can complicate experimental outcomes, detailing the pathways by which these substances may interact with both the experimental environment and the animals themselves. We conclude that there is widespread potential for serious complications to research outcomes and chronic toxicity from enrichment materials. Therefore, we advocate for the establishment of standardized regulations and a global certification system for aquatic enrichment items to ensure the validity of studies and to safeguard animal welfare. We encourage researchers to critically consider the implications of leaching from aquatic enrichment when designing experimental systems.
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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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".