A Minimum Data Set of Animal Health Laboratory Data to allow for Collation and Analysis across Jurisdictions for the Purpose of Surveillance
Bibliographic record
Abstract
A minimum data set consisting of 15 data elements originating from laboratory submissions and results was formulated by a national committee of epidemiologists in Canada for the purposes of disease reporting, disease detection and analysis. The data set consists of both data that are filled out on the submission form as well as the results of the laboratory testing. The elements in the data set are unique identifier, premises identification, date submitted, geographic location, species, farm type, group type, total population of tested species on the farm, number sick, number dead, test(s) performed, disease agent, test result, disease classification by submitter and final laboratory diagnosis. The data set was designed to be concise while allowing for domestic and international disease reporting, effective analysis, including geographic, temporal and prevalence outputs, and syndromic surveillance to enable disease detection. The selected data elements do not identify the producer as specific geographic and nominal information is not included in the data set. The data elements selected, thus, allow for voluntary collaboration and data sharing by avoiding issues associated with privacy legislation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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 teacher head, 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".