A Pilot Study Using Remote Digital Necropsy Images to Diagnose Gross Lesions in Swedish Cattle
Bibliographic record
Abstract
Remote digital necropsy (RDN) in cattle is a systematic method of post mortem diagnostic remotely, with the aim of increasing knowledge about deaths. The method is based on a standardized, systematic approach to opening and photo documenting dead cattle on farm. Images are sent to a bovine practitioner, experienced in interpreting gross lesions, for analysis. In this pilot study, education and training sessions on the RDN method were administered to veterinarians. After the training sessions, the participants performed 15 RDNs. Subsequently, interviews were conducted with 10 participants to capture their experiences and views on using the RDN method. In many of the necropsied animals, the RDN diagnosis matched well with the on-site veterinarian’s observations. The participants expressed a great interest in developing and obtaining more skills in performing on-farm necropsies. This pilot study identified several challenges with the RDN and these need to be considered before implementing this service for Swedish cattle farms. The pilot study also showed that the RDN method could be a good complement to the regular post-mortem examinations and that it is possible to use the RDN in Sweden to determine a gross diagnosis of the cause of death of cattle, with subsequent increased disease surveillance.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".