127- I levels in Ontario Bulk Tank Milk and its Association with Groundwater, Milking Management, and Other Risk Factors
Bibliographic record
Abstract
The objectives of the present study were to determine the iodine concentration in milk sampled from 80 commercial dairy farms located in eastern (n=58) and southwestern (n=22) Ontario, and to identify if the iodide content of groundwater consumed by the lactating herd, along with other factors, are associated with higher bulk milk iodine (BMI). A bilingual questionnaire addressing water consumption, nutrition, milking management practices, and well characteristics was completed by each producer. The 127I concentration in milk and groundwater samples was established using inductively coupled plasma mass spectrometry. Potential predictor variables were screened for univariable significance (p0.05), and a general linear regression model was fitted to assess associations between BMI and explanatory variables such as 127I in water, well depth and age, water treatment, the use of iodine-based pre or post-dips, application strategies and post-dip coverage goal. Results of the data analysis suggest that there is a strong positive correlation between the iodide content of groundwater and BMI levels (p0.001). Post-milking practices including the use of an iodine-based teat disinfectant, the application strategies of a post-milking teat disinfectant and overall coverage goal of the solution on teats were also significantly (p0.05) associated with increased BMI levels. These results suggest a significant association exists between BMI levels in relation to post-milking management practices and the iodine content in groundwater consumed by the lactating herd. Post-milking practices and drinking water containing excessive levels of iodine should be monitored frequently and where needed mitigation strategies implemented to prevent high BMI levels on farm.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".