Internet data services from maritime quality milk: tools for tracking milk quality
Bibliographic record
Abstract
Milk quality monitoring has been an important aspect of public health programs for more than 50 years. Bulk tank milk samples are typically collected on dairy farms at every tanker truck pickup. Information generated from samples is not fully exploited to aid the producer in managing their herds. There is often a gap between generation of the data and communication of the information to make decisions and improvements on dairy farms. In a recent project, consumer complaints were decreased by half when milk was sourced from farms meeting a higher bacteriologic and bulk tank somatic cell count standard. During that project we determined that dairy producers were not efficiently using milk quality data generated through the regulatory system. This may be because the data was not in a format that could be quickly and easily understood. Subsequently, Maritime Quality Milk began providing value-added graphical analysis of milk quality and component parameters for producers in the Eastern Canadian provinces of Prince Edward Island, Nova Scotia and New Brunswick. Producers are able to look at quality results in real time or they can control the level of monitoring effort expended by setting email device notification filters. Using these systems, farmers are alerted only when test performance have gone outside the parameters that they have set. More recently, one processor has begun using the system to provide real-time alerts to producers regarding quality targets they must achieve on a monthly basis to qualify for quarterly bonuses. In 2011, focus groups were conducted to understand the industry needs, barriers to use and set development priorities.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".