Animal‐related factors predicting fatal cases of blackleg and gas gangrene in cattle
Bibliographic record
Abstract
BACKGROUND: Blackleg and gas gangrene are acute clostridial infections primarily affecting cattle. The objectives of this study were to identify (i) animal-related factors influencing the occurrence and (ii) prognostic pathological findings supporting the differentiation of fatal blackleg and gas gangrene cases in the cattle population from 1998 to 2018 in Styria, Austria. METHODS: Two binomial logistic models were applied to analyse the research questions. Additionally, cross-validations were performed to estimate the accuracy of the predictive models. RESULTS: Model results show that animal-related factors (i.e., age, geographical discovery location of dead cattle, vaccination status) significantly influence the occurrence of blackleg when compared to gas gangrene. Pathological findings are similar for both diseases. CONCLUSIONS: Model results reveal that using animal-related factors has a better accuracy to predict the fatal cases caused by both pathogens. Thus, the authors recommend not relying on pathological findings as predictive factors in the differentiation between blackleg and gas gangrene in cattle.
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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 | 0.002 | 0.005 |
| 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".