Performance of a predictive computer model to simulate gastrointestinal nematode epidemiology on Ontario sheep farms
Bibliographic record
Abstract
This thesis describes an investigation into the farm-level performance of an existing predictive sheep parasite model from the United Kingdom, using Canadian data. The model simulated the epidemiology of three major gastrointestinal nematode species ('Teladorsagia' sp., 'Haemonchus' sp. and 'Trichostrongylus' spp.), and provided seasonal parasite predictions for lambs and ewes. Required input data included ewe parasite egg output, pasture-related information, and management dynamics. Monthly farm visits conducted in 2006 and 2007 for another project, supplied relevant input data. These visits also provided observed values to which model outputs were compared and assessed using regression analysis. For 23 Ontario farms with available data, 12 and 15 farms had suitable data to run the model for 2006 and 2007, respectively. Amongst these farms, 5 of 12 (42%) and 7 of 15 (47%) farms showed reasonable fit (i.e. R2>50%). Data which did not fit the model were explained by atypical management and climate factors.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".