Effects of iodine intake and teat-dipping practices on milk iodine concentrations in dairy cows
Bibliographic record
Abstract
Two studies were conducted to determine the effects of dietary iodine and teat-dipping practices on iodine concentrations in milk. In the first study, 63 cows in mid lactation were assigned to a 3×3 factorial design in which the main effects were dietary iodine levels (0.3, 0.6, and 0.9 mg of dietary I/kg of dry matter) and 3 different postdip managements (chlorhexidine with dip cup, 1% iodine dip cup, and 1% iodine by manual spray). During the 13-d pre-experimental period and the 15-d experimental period, noniodized sanitizers were used in premilking management. During the pre-experimental period, the levels of milk iodine averaged 241.2±5.8 μg/kg, and no relationship was found with lactation number, days in milk, or milk production. Milk iodine concentrations increased linearly with iodine intake. Although teat dipping with 1% iodine had no effect on milk iodine concentration, the same solution applied by spraying greatly increased milk iodine levels. The second study was conducted to determine the effects of udder preparation before milking on milk iodine concentrations. Thirty-two lactating cows were assigned to 4 treatments: no predip (Con); predip with a predip solution containing 0.5% iodine+complete cleaning (Comp); predip with a postdip solution containing 1% iodine+complete cleaning (Post); and predip with a predip solution containing 0.5% iodine+incomplete cleaning (Inc). During the 14-d pre-experimental period and the 19-d experimental period, cows were fed the same diet, and noniodized sanitizers were used for postmilking dipping. During the last week of treatment, milk iodine averaged 164, 189, 218, and 252±9.8 μg/kg for Con, Comp, Post, and Inc, respectively. Preplanned orthogonal contrasts indicated that predipping with a 0.5% iodine predip solution completely wiped off (Comp) tended to increase milk iodine content above that of the control and that the iodine content of Post and Inc were higher than that of the Comp treatment. The results of the first experiment confirm that, to preserve milk safety, iodine should not be fed above requirements. Spraying iodine-based teat-dipping solutions results in large increases in milk iodine content and should be avoided. Predipping teats with an iodine-based sanitizer is an acceptable practice, but must be performed with the appropriate product and completely wiped off before milking.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.001 |
| 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 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".