Foot and mouth disease model verification and 'relative validation' through a formal model comparison
Bibliographic record
Abstract
(1) AsureQuality Limited, P.O. Box 585, Palmerston North 4440, New Zealand (2) Department of Computing and Information Science, University of Guelph, Guelph, Ontario N1G 2W1, Canada (3) Office of the Chief Veterinary Officer, Department of Agriculture, Fisheries and Forestry, G.P.O. Box 858, ACT 2601, Australia (4) EpiCentre, Institute of Veterinary, Animal and Biomedical Sciences, Massey University, Private Bag 11-222, Palmerston North, New Zealand (5) Institute of Fundamental Sciences, Massey University, Private Bag 11-222, Palmerston North, New Zealand (6) Embassy of Ireland, Piazza di Campitelli 3, 00186 Rome, Italy (7) Animal Health and Management Section, Canadian Food Inspection Agency, 59 Camelot Drive, Ottawa, Ontario K1A 0Y9, Canada (8) Centers for Epidemiology and Animal Health, 2150 Centre Avenue, Building B, Fort Collins, Colorado, United States of America (9) MAF Biosecurity New Zealand, Pastoral House, P.O. Box 2526, Wellington, New Zealand
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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 | 0.034 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".