The use of hair as a long‐term indicator of low‐dose β<sub>2</sub> agonist treatments in cattle: Implications for growth‐promoting purposes monitoring
Bibliographic record
Abstract
Abstract The objective of this study was to assess the feasibility of using hair as a long‐term indicator of cocktail (low‐dose β2 agonists) treatments in cattle. Six male Simmental cattle were treated with a mixture of low‐dose clenbuterol, ractopamine, and salbutamol at dosages of 5.3, 223.3, and 50.0 μg/kg, respectively. The trial lasted for 112 days and included 28 days of treatment and 84 days of withdrawal. Plasma and urine samples taken during the treatment period contained the highest residues, with maximum concentrations of clenbuterol, ractopamine, and salbutamol in plasma of 1.49 ng/mL (Day 21), 43.78 (Day 14) ng/mL, and 8.07 ng/mL (Day 7), respectively, and in urine of 62.40 ng/mL (Day 28), 3995.77 ng/mL (Day 28), and 503.72 ng/mL (Day 1), respectively. On day 42 of withdrawal, the residues of all three β2 agonists in plasma were below the limit of quantification (LOQ; 0.3 ng/mL for clenbuterol, and 0.5 ng/mL for ractopamine and salbutamol), and in urine samples were below or near the LOQ (the highest being ractopamine at 1.10 ng/mL). The highest concentrations of clenbuterol, ractopamine, and salbutamol in hair were 88.36, 1351.92, and 100.58 ng/g, respectively, on day 14 of withdrawal; and the residues were long‐lasting, with 7.64, 28.55, and 8.77 ng/g, respectively, on day 84 of withdrawal. The results of this study demonstrate that hair could be utilized as a long‐term indicator of the use of a combination of low‐dose β2 agonists in cattle, which could have implications for growth‐promoting purposes monitoring.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".