Developing a Biomarker to Cold Shock in the Yellow Perch, Perca flavescens
Bibliographic record
Abstract
Fish kills are common events with their causes variously attributed to natural and anthropogenic sources.Yet, it is very difficult to properly attribute a specific cause to fish death due to many interacting variables.Utilities have found a similar problem where fish impingement events coincide with rapid reductions in water temperature.There is no clear protocol for investigating this matter to determine if cold shock is the underlying mechanism for impingement.Hence, this study focused on developing a biomarker for cold shock in fish.In this thesis I used the expression of twenty-eight genes (grouped into four categories of: stress response, endocrine disruption, growth and metabolism, and immune function genes) in yellow perch (Perca flavescens) to isolate between common stressors (heat and air exposure) and varying degrees of cold shock (exposures of 8°C, 12°C, and 16°C).I also assessed gene expression in different cold shock exposures (8°C, 12°C, and 16°C) over time (~4-5 fish sampled/hour for a maximum of ~5 hours).Several potential biomarkers for cold shock were identified: cirbp, igf1, igf2, cam, and mhcI.The expression of markers was also found to express differentially between different cold temperatures and at a single cold temperature over time.The findings of this study contribute to the knowledge gap in the literature for the field of cold shock in fish and provides a novel study where many proposed markers in the field were tested together for biomarker identification.Benjamin, and Joshua Cooke provided expert angling services which ensured a continuous supply of yellow perch for experimentation.The field experimentation took place at Queen's University Biological Station (QUBS), so I thank their team and the broader QUBS community for their support and assistance.Along with this, I would like to thank all my friends and family for their relentless support and guidance.You all mean the world to me, and I am very lucky to have the support network that I do.Last but not least, I would like to thank whole grain Cheerios for allowing me to snack during this process.
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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".