Development of a 17-plex STR typing system for the identification of individuals and parentage testing in cattle
Bibliographic record
Abstract
Accurate identification of animals and the verification of their parentage can be used to pedigree populations and support selective breeding. The International Society for Animal Genetics recommended 16 cattle STRs for individual identification and parentage testing in cattle, but no multiplex STR typing system contains these 16 STRs. Here, we develop an efficient 17-plex multiplex typing system for cattle that contains the 16 ISAG recommend STRs and a sex-determining marker. Compared to the Bovine Parenting Typing Kit (containing 11 of the 16 ISAG recommend STRs), our new typing system not only increases the number of molecular markers, but also simplifies the PCR operation and shortens the time for the typing procedure (from 4.5 h to 1 h 37 min). Profile can be generated from a single PCR reaction using as little as 1 ng of DNA. The combined probabilities of paternity exclusion CPE duo and CPE trio were 0.999804697 and 0.999999260, respectively. These results indicate that our 17-plex typing system is a fast, sensitive and species-specific method for the identification of individuals and their parentage for cattle. The application of this system will improve the efficiency of the identification of cattle individuals and their paternity, supporting population genetic research and the selective breeding of 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".