Risk factor determination and qualitative risk assessment of Mucormycosis in Harbor Porpoise, an emergent fungal disease in Salish Sea marine mammals
Bibliographic record
Abstract
Mucorales infections are increasing in frequency and are a One Health pathogen of concern. In humans and domestic animals, risk factors include being immunocompromised, elevated circulating serum iron, contaminated open wounds, or metabolic diseases such as ketoacidosis or uncontrolled diabetes. Mucormycosis was first identified in 2012 in Pacific Northwest marine mammals, predominantly in harbor porpoises. We performed an assessment to determine the overall qualitative risk, or risk score, of mucormycosis in harbor porpoises. Risk factors for this disease are unknown in aquatic mammals. In a separate risk factor analysis, potential risk factors such as pollutants, trace metals (e.g., iron), and co-infection with other pathogens (e.g., viruses and Brucella spp.) were examined in mucormycosis cases and noncases using a matched case-control study design, to determine the presence and strength of association of these factors with mucormycosis. Disease severity (gross and histopathology) and exposure scores were multiplied together to obtain the overall risk scores of 9 -16 which corresponded to moderate and severe, respectively. In the risk factor analysis, the factors most strongly associated with a mucormycosis case, relative to a control, were elevated liver iron, decreased blubber thickness, and the decreased ratio of the sum of PCB congeners/sum of PBDE congeners. The results of this study suggest that mucormycosis may pose an inordinately high risk to harbor porpoises (and potentially sympatric species in the Salish Sea such as southern resident killer whales) based on the detected prevalence and the severity of lesions observed at necropsy. However, the risk may be greater on an individual basis compared to the overall population, and is likely related to other factors such as increased POP and heavy metal burdens.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".