A comparison of infectious agents between hatchery-enhanced and wild out-migrating juvenile chinook salmon (<i>Oncorhynchus tshawytscha</i>) from Cowichan River, British Columbia
Bibliographic record
Abstract
Infectious diseases are likely contributing to large-scale declines in chinook salmon stocks in the Pacific Northwest, but the specific agents and diseases involved, and the prevalences in migratory salmon, are mostly unknown. We applied a high-throughput microfluidics platform to screen for 45 infectious agents in 556 out-migrating juvenile chinook salmon, collected from freshwater (FW) and saltwater (SW) locations in the Cowichan River system on Vancouver Island, Canada, during 2014. Nineteen agents (5 bacterial, 2 viral, and 12 parasitic) were detected, with prevalences ranging from 0.2% to 57.6%. Co-infections between Candidatus Branchiomonas cysticola Toenshoff, Kvellestad, Mitchell, Steinum, Falk, Colquhoun & Horn, 2012, Paranucleospora theridion Nylund, Nylund, Watanabe, Arnesen & Kalrsbakk, 2010, and gill chlamydia, all associated with gill disease, were observed in SW samples. We detected agents known to cause large-scale mortalities in Pacific salmon ( Ceratonova shasta (Noble, 1950), Parvicapsula minibicornis Kent, Whitaker & Dawe, 1977), and agents only recently reported in Pacific salmon in BC ( Ca. B. cysticola, P. theridion, Facilispora margolisi Jones, Prosperi-Porta & Kim, 2012 and Parvicapsula pseudobranchicola Karlsbakk, Saether, Hostlund, Fjellsoy & Nylund, 2002). Wild and hatchery fish were most divergent in agent profiles in FW, with higher agent diversity in wild fish. Differences in prevalence largely dissipated once they converged in the marine environment, although hatchery fish may be infected by a greater diversity of agents sooner after ocean entry by virtue of their more rapid migration from nearshore to offshore 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".