Gene expression in caged fish as a first-tier indicator of contaminant exposure in streams
Bibliographic record
Abstract
The development of sensitive, biologically based indicators of contaminant exposure (i.e., biomarkers) is an ongoing topic of research. These indicators have been proposed as a first-tier method of identifying contaminant exposure. The primary objective of this research was to implement a biomarker-based method of exposure assessment using caged fish and real-time reverse-transcriptase polymerase chain reaction (rtRT-PCR) measurements of gene expression. Primers were developed for the CYPIA, metallothionein, and vitellogenin genes in rainbow trout (Oncorhynchyus mykiss), cutbow trout (Oncorhynchyus clarkii x mykiss), and Atlantic salmon (Salmo salar). Each of these genes has been shown to respond specifically to planar aromatic compounds, heavy metals, and environmental estrogens, respectively. Juvenile fish were placed in cages and exposed in situ at reference and contaminated sites on the Cache la Poudre River (CO, USA), the Arkansas River (CO, USA), the St. John River (NB, Canada), and two urban creeks near Dayton (OH, USA). Quantitative gene expression was determined using rtRT-PCR. Biomarker expression profiles were obtained that demonstrated differences in CYPIA, metallothionein, and vitellogenin mRNA production unique to each site, indicating that specific types of compounds were bioavailable and present in sufficient concentrations to elicit transcriptional responses in the organism. These findings support the use of a biomarker-based approach to exposure identification and assessment.
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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".