Insights gained through real-time monitoring of porcine reproductive and respiratory syndrome virus and description of temporal trends based on laboratory data in Ontario, Canada
Bibliographic record
Abstract
Porcine reproductive and respiratory syndrome virus (PRRSV) is a prevalent pathogen that impacts the health of swine and is costly to the swine industry. This study utilized PRRSV test results from the University of Guelph's Animal Health Laboratory database to develop interactive, real-time dashboards and to monitor and investigate PRRSV data. The test results from Ontario swine herd samples submitted from January 2014 to July 2023 were processed in R v.4.1.1. The final optimized, aggregated, and anonymized datasets were exported to the Tableau server and were used to design dynamic real-time visualizations with Tableau Desktop v.2021.4. Constructed dashboards were: (1) monthly number of submissions and positive submissions over the last 10 years; (2) number of submissions and positive submissions over the last 3 years, interactively displayed at weekly, monthly, quarterly and yearly intervals; (3) monthly number of PRRSV restriction fragment length polymorphism pattern (RFLP) types at the submission level over the last 5 years; (4) weekly number of tested farms and positive farms over the last 6 Years; (5) monthly number of tested farms and positive farms over the last 6 Years; (6) indicators of the epidemiological data quality in each month; and (7) contextual information. Eighty different PRRSV RFLP patterns were identified with the predominant patterns being 1-8-4, 1-1-1, 1-4-2, and 2-5-2. Most farms contributed one submission per week or per month for PRRSV testing (median: 1 submission per week; IQR: 0; max: 13; median: 1 submission per month; IQR: 1, max: 31). Epidemiological data quality showed considerable improvements over the 9 years of investigation. Apparent changes in trends of submissions were visually observed when time series were stratified by reasons for submission and production class.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 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".