A Comparison of Nonlethal and Destructive Methods for Broad-Based Infectious Agent Screening of Chinook Salmon Using High-Throughput qPCR
Bibliographic record
Abstract
Molecular tools, such as high-throughput quantitative polymerase chain reaction (HT-qPCR), are useful for monitoring multiple infectious agents in wild animal populations (i.e., broad-based screening). If destructive tissue samples cannot be obtained due to experimental design requirements (e.g., bio-telemetry; holding with repeated biopsy) or the conservation status of host species, then nonlethally sampled tissues can be substituted. However, infection profiles have been found to differ between nonlethally and destructively sampled tissues. We present a comparative analysis of nonlethal (gill and blood) and destructive (pool of internal and external tissue) approaches for broad-based infectious agent screening of adult Chinook Salmon Oncorhynchus tshawytscha. Of a possible 47 agents, 16 were detected overall by nonlethal and destructive methods. Our results indicated moderate differences in infection profiles among tissues, with limitations of each tissue type dependent on the ecology of each agent. The gill was the most comprehensive screening tissue, as more infectious agents were detected overall in gill (n = 16) than in blood (n = 12) or multi-tissue pools (n = 15). The agreement in the estimated agent prevalence between tissue types ranged from poor to excellent, while overall agent community structure (the combined prevalence of all agents) showed low agreement between tissue types. Two agents occurred at 100% prevalence in all tissue types. Nine agents, including types of bacteria and gill parasites, were more prevalent in gill than in blood, while five agents, including one virus and several microparasites, were more prevalent in blood. Future studies should pair microscopy and histopathology with HT-qPCR to better characterize host health and disease development relative to molecular detection of agents across tissue types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 teacher head, 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".