Can risk rating tool results be used to predict results of inspection categories of dairy processing plants?
Bibliographic record
Abstract
Background: Dairy products are consumed by a large portion of the population. The dairy processing plants (DPP) that produce these perishable products may create health hazards (chemical, physical, biological). In order to minimize any health risks from these products, DPP are inspected by regulating authorities. This study examined secondary data derived from the BCCDC dairy program’s semi-quantitative risk ranking tool (RRT) to examine trends over time with DPP inspections, and to assess risk factors within the tool. Methods: RRT based data from individual DPP inspections from 2015 through 2018 were entered into a master spreadsheet. The RRT has two overall risk categories, inherent and measured risk. Inherent risk categories in the tool were sourced from surveys of dairy plants, while measured risks in the tool were sourced from inspection visits (routine and in-depth), environmental and food result submissions from dairy plants and inspectors, and based on compliance and history. In total, 107 items were assessed within the eight categories. Descriptive analyses were conducted, and statistical analyses performed using NCSS 12 software (NCSS, 2018). Results: A total of 128 inspection reports from 30 different DPP were included in this study. From these inspections, 65% were considered low risk, 12% moderate and 23% high risk. DPP that were located on-farm were found to have significantly higher overall inspection risk scores than dairy plants located off-farm (average on-farm inspection risk ranking score = 694; average off-farm inspection risk ranking score = 153; p=0.0003, power=95%). When the microbiological scores category, derived from environmental swabs and food submissions, were compared to the inspection score category, these categories were statistically significantly correlated (p=0.0000, power=100%); when inspection score increases, so too does microbiological score. Higher risk scores were also found in DPP producing more than one category of dairy product (comparing one product versus 6 or 7 products, p=0.009, power=76%). Conclusion: Dairy inspections ensure DPP follow good manufacturing practices and therefore help to protect the population from disease outbreaks or other contaminations. This study demonstrated that there is increased risk of having a dairy processing facility located on-farm, that more complex dairy processing operations that produce more than one type of dairy product have higher risk rating scores and that higher inspection score violations positively correlated to positive microbiological scores. This study further showed that in the absence of microbiological results, a risk score could still be calculated by analyzing the inspection violations alone. The Food Safety Specialists at the BCCDC can use this data to focus their inspection time on higher risk areas and items to maximize time spent out in the field.
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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 | 0.014 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".