Using time-temperature recorder data on dairy farms to identify short-term factors associated with increased free fatty acids in bulk tank milk
Bibliographic record
Abstract
Elevated concentrations of free fatty acids (FFA; ≥1.20 mmol/100 g of fat) reduce milk quality by changing milk sensory and functional properties. Bulk tank FFA levels vary between milk pickups, and there is limited research to identify factors associated with these short-term fluctuations in FFA. A time-temperature recorder (TTR) may be used on dairy farms to identify milk quality concerns through producer alarms related to milk cooling, storage, and contact surface sanitization. The objective of this study was to investigate whether specific TTR alarms could be associated with short-term increases in bulk tank milk FFA. An observational cross-sectional study was conducted, and data from TTR units on 177 farms in Ontario, Canada, were collected and analyzed. A subset of 751 alarms from 120 farms was used. For each alarm, the baseline FFA concentration (average FFA across 7 pickups before the alarm, if no alarm present) was compared with the alarm-associated FFA. The average FFA for all bulk tank milk samples associated with any single TTR alarm (0.90 ± 0.50 mmol/100 g of fat) was greater than the baseline average FFA (0.88 ± 0.41 mmol/100 g of fat). More than 15% (n = 113) of TTR alarm-associated samples were elevated (≥1.20 mmol/100 g of fat). Observations from a bulk tank sample with a "milk too cold" alarm had the highest average FFA (1.31 mmol/100 g of fat) and were the only ones with a significant increase in FFA compared with the baseline. Using a mixed linear regression model, the associated increase in FFA was 0.36 mmol/100 g of fat when a "milk too cold" TTR alarm occurred. The absence of additional cooling was also a risk factor for increased FFA and a "milk too cold" alarm. These results suggest that a "milk too cold" TTR alarm could help explain short-term increases in FFA on dairy farms with bulk tank milk temperature recording.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".