PSIII-14 Infrared thermography as a tool to detect inflammation in feedlot lambs with footrot
Bibliographic record
Abstract
Abstract Infrared thermography (IRT) has been used as a non-invasive tool to detect inflammatory processes associated with disease in livestock. The aim of this study was to evaluate IRT as a tool to compare healthy and footrot (FR) affected hooves in feedlot lambs with varying degrees of lameness over two seasons. A total of 106 lame lambs with footrot from a feedlot in Alberta were individually categorized according to a 3-point locomotion scale [1 = mild (n = 7), 2 = moderate (n = 46) and 3 = severe lameness (n = 53)] during the summer (n = 39) and fall (n = 68) of 2018. All lambs were physically examined once by two experienced observers to determine if the lamb had footrot. IRT images of the interdigital space were used to obtain the maximum hoof temperature (MHT) of both FR affected as well as healthy (CT) hooves within the same animal. Generalized linear mixed models (SAS PROC GLIMMIX) were performed separately for each season and diagnosis and included locomotion score as a fixed effect and ambient temperature as a co-variate. Predicted means were compared using the limits at 95% of confidence. Overall, greater MHT (P < 0.05) were observed for FR affected compared to unaffected hooves for lambs categorized as moderately and severely lame, within each season. However, no differences (P > 0.05) in MHT were observed for lambs categorized as mildly lame, likely due to the small number of lambs having a locomotion score of 1. Under the conditions of this study, thermal images can be effectively used as a tool to distinguish footrot affected hooves in feedlot lambs with moderate and severe lameness. Further studies should be conducted with more lambs with a locomotion score of 1 to assess the relationship between mild lameness, IRT, and footrot diagnosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".