Development of an aquatic pathogen database (AquaPathogen X) and its utilization in tracking emerging fish virus pathogens in North America
Bibliographic record
Abstract
The AquaPathogen X database is a template for recording information on individual isolates of aquatic pathogens and is freely available for download (http://wfrc.usgs.gov). This database can accommodate the nucleotide sequence data generated in molecular epidemiological studies along with the myriad of abiotic and biotic traits associated with isolates of various pathogens (e.g. viruses, parasites and bacteria) from multiple aquatic animal host species (e.g. fish, shellfish and shrimp). The cataloguing of isolates from different aquatic pathogens simultaneously is a unique feature to the AquaPathogen X database, which can be used in surveillance of emerging aquatic animal diseases and elucidation of key risk factors associated with pathogen incursions into new water systems. An application of the template database that stores the epidemiological profiles of fish virus isolates, called Fish ViroTrak, was also developed. Exported records for two aquatic rhabdovirus species emerging in North America were used in the implementation of two separate web-accessible databases: the Molecular Epidemiology of Aquatic Pathogens infectious haematopoietic necrosis virus (MEAP-IHNV) database (http://gis.nacse.org/ihnv/) released in 2006 and the MEAP- viral haemorrhagic septicaemia virus (http://gis.nacse.org/vhsv/) database released in 2010.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".