Electronic identification: Applications in beef production and research
Bibliographic record
Abstract
Individual identification of beef cattle is not new to the Canadian beef industry, as traceback systems played a pivotal role in the eradication of bovine tuberculosis in the 1940s and 1950s and brucellosis in the 1970s and 1980s. Recent concerns over animal health (e.g., bovine spongiform encephaolopathy), export markets, product consistency, meat quality (e.g., tenderness, marbling) and safety (e.g., Escherichia. coli 0157:H7, Salmonella spp.) make reestablishment of a traceback system a logical approach to assuring consumer confidence in Canadian beef. Originally, simple Kurl-lockTM ear tags with a unique number were used to trace individuals back to their herd of origin. Although useful for addressing disease concerns, this system did not lend itself to compiling additional information (e.g., growth performance, animal health, breeding programs, carcass quality) for use in management or marketing decisions. More sophisticated electronic identification systems can readily interface with computers and make information management an even more pivotal component of beef production. Several electronic identification systems (e.g., bar codes, radio frequency identification, read–write systems) are being assessed for their effectiveness for identifying individual cattle under production conditions. In research applications, this technology has the potential for individual animals to become the experimental unit under group housing conditions. By combining electronic identification technology with devices that measure physiological (e.g., temperature, pH, body weight, feed intake) parameters, researchers will be able to collect data in natural production environments that were previously only obtainable under controlled experimental conditions with a limited number of animals. Key words: Electronic identification, beef, traceback, radio frequency identification
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".