How Does Reference Site Selection Influence Interpretation of Omics Data?: Evaluating Liver Transcriptome Responses in Male Rainbow Darter (<i>Etheostoma caeruleum</i>) across an Urban Environment
Bibliographic record
Abstract
Studies quantifying the influence of reference site selection on transcriptomic profiles in aquatic organisms exposed to complex mixtures are lacking in the literature, despite the significant implications of such research for the interpretation of omics data sets. We measured hepatic transcriptomic responses in fish across an urban environment in the central Grand River watershed (Ontario, Canada). Adult male rainbow darter (RBD) (Etheostoma caeruleum) were collected from nine sites at varying distances from two major municipal wastewater treatment plants (MWWTPs) (Waterloo, Kitchener), including three upstream reference sites. The transcriptomic response in RBD was independently compared with that of fish from each of the three reference sites. Data collected in fish downstream of the Waterloo MWWTP (poorest effluent quality) suggested that ∼15.5% of the transcriptome response was influenced by reference site selection. In contrast, at sites where the impact of MWWTPs was less-pronounced and fish showed less of a transcriptome response, reference site selection had a greater influence (i.e., ∼56.9% of transcripts were different depending on the site used). This study highlights the importance of conducting transcriptomics studies that leverage more than one reference site, and it broadens our understanding of the molecular responses in fish in dynamic natural environments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".