MoniQA: an update of the European Union funded Network of Excellence in 2011
Bibliographic record
Abstract
Monitoring and quality assurance in the total food supply chain (MoniQA, http://www.moniqa.org) is an EU-funded Network of Excellence (NoE), which involves experts from around the globe working for safer foods though harmonization of worldwide food quality and safety monitoring and control strategies. MoniQA is coordinated by the International Association for Cereal Science and Technology (ICC, http://www.icc.or.at), and the initial network of around 150 scientists from 20 countries has grown to more than 500 experts from some 40 countries across five continents. The consortium has committed its knowledge, international relationships, and communication resources to providing reliable information, globally agreed standards and tools to ensure safe foods, to support regulatory bodies in developing better regulations, and food manufacturers in the production of high-quality food and achieving legal compliance. MoniQA focuses on validation of methods used to analyse foods and food products for safety and quality. The main emphasis is on rapid methods and emerging new technologies, their applicability and reliability in routine testing. The work involves validation studies, design and development of reference materials, and validation guidelines, as well as socio-economic impact assessment for better future regulations. MoniQA's outputs will impact society at several levels, including research and development, industry and SMEs in food manufacturing, retail and food analysis, regulators, policy makers, associations, international organizations and consumers as well as international trade. Progress towards the goals of MoniQA and response to the original drivers for this project are considered, and MoniQA's sustainability plans are described in more detail by highlighting some selected outputs.
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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 | 0.063 | 0.045 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.021 | 0.012 |
| Open science | 0.010 | 0.016 |
| Research integrity | 0.008 | 0.004 |
| Insufficient payload (model declined to judge) | 0.036 | 0.039 |
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".