Use of random amplified microsatellites (RAMS) to discern genotypes of Saprolegnia parasitica isolates on the west coast of British Columbia
Bibliographic record
Abstract
Several oomycete species of the genus Saprolegnia are recognized as devastating fish pathogens and are responsible for the loss of millions of fish annually for the aquaculture industry. Until recently, these pathogens were kept in check using malachite green; however, due to its toxicity, this chemical has now been banned from use. Saprolegnia parasitica is recognized as the major pathogen of aquaculture fish species. The industry is struggling to predict and control S. parasitica outbreaks in fish hatcheries and there is a need for new knowledge regarding the population genetic structure of this pathogen. Random amplified microsatellites were used to compare isolates of S. parasitica collected from a variety of hatchery locations during the period of November 2009 - August 2011, in order to determine the level of genetic variability and determine changes in genetic diversity over time. Allele frequencies of scored characters were graphically compared. Population genetic diversity was measured using Nei’s genetic distance, Shannon’s Information Index, number of polymorphic loci and phylogenetic trees. Due to the presence of Saprolegnia parasitica in the facilities tested, it appears to be ubiquitous in aquaculture facilities and treatment and prevention will be an ongoing concern in aquaculture management. Overall, genetic diversity of S. parasitica isolates was determined to be low with at least some sexual recombination occurring over time. There was a diversity of genotypes collected from the same hatchery on a single day, indicating there was not a single genotype present at a given time point. Genetic profiling, such as used here, could provide facility managers with a new approach to develop a series of best practices to control sporadic outbreaks of disease. Use of these genetic markers and close monitoring of S. parasitica genotypes will permit early detection and sanitation protocols.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".