Comparison of beta-ligands used in cattle production: structures, safety, and biological effects
Bibliographic record
Abstract
Technologies that increase the efficiency and sustainability of food animal production to provide meat for a growing population are necessary and must be used in a manner consistent with good veterinary practices, approved labeled use, and environmental stewardship. Compounds that bind to beta-adrenergic receptors (β-AR), termed beta-adrenergic receptor ligands (β-ligands), are one such technology and have been in use globally for many years. Though all β-ligands share some similarities in structure and function, the significance of their structural and pharmacological differences is sometimes overlooked. Structural variations in these molecules can affect absorption, distribution, metabolism, and excretion as well as cause substantial differences in biological and metabolic effects. Several β-ligands are available for use specifically in cattle production. Ractopamine and zilpaterol are beta-adrenergic agonists approved to increase weight gain, feed efficiency, and carcass leanness in cattle. They both bind to and activate β1- and β2-AR. Lubabegron is a newly developed selective beta-adrenergic modulator with unique structural and functional features. Lubabegron displays antagonistic behavior at the β1- and β2-AR but agonistic behavior at the β3-AR. Lubabegron is approved for use in cattle to reduce ammonia emissions per unit of live or carcass weight. Additionally, lubabegron can withstand prolonged use as the β3-AR lacks structural features needed for desensitization. Due to these unique features of lubabegron, this new β-ligand provides an additional option in cattle production. The individual properties of each β-ligand should be considered when making risk management decisions, as unique properties result in varying human food safety profiles that can determine appropriate safe β-ligand use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".