Developing molecular classifiers to detect environmental stressors, smolt stages and morbidity in coho salmon, Oncorhynchus kisutch
Bibliographic record
Abstract
Aquatic species are increasingly confronted with environmental stressors because of climate change. Although molecular technologies have advanced our understanding of how organisms respond to stressors in laboratory settings, the ability to detect physiological responses to specific stressors under complex field conditions remains underdeveloped. This research applied multi-stressor challenge trials on coho salmon, employing the "Salmon Fit-Chips" genomic tool and a random forest-based classification model to develop classifiers predictive for chronic thermal and hypoxic stress, as well as salinity acclimation, smolt stage and morbidity status. The study also examined how smolts and de-smolts (smolts not having entered SW during the smolt window) responded transcriptionally to exposure to saltwater. Using RF classifiers optimized with 4 to 12 biomarkers, we identified transcriptional signatures that accurately predicted the presence of each stressor and physiological state, achieving prediction accuracy rates between 86.8 % and 100 %, regardless of other background stressors present. Stressor recovery time was established by placing fish back into non-stressor conditions after stress exposure, providing important context to stressor detections in field applications. Recovery from thermal and hypoxic stress requires about 3 and 2 days, respectively, with >3 days needed for re-acclimation to freshwater for seawater acclimated fish. The study also found non-additive (synergistic) effects of multiple stressors on mortality risk. Importantly, osmotic stress associated with de-smolts was the most important predictor of mortality. In saltwater, de-smolts exposed to salinity, high temperature, and hypoxia experienced a 9-fold increase in mortality compared to those only exposed to saltwater, suggesting a synergistic response to multiple stressors. These findings suggest that delays in hatchery releases to support release of larger fish need to be carefully scrutinized to ensure fish are not being released as de-smolts, which are highly susceptible to additional climate-induced stressors like rising temperatures and reduced dissolved oxygen levels in the marine environment.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 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".