Food safety: contaminants and toxins
Bibliographic record
Abstract
PART I: BIOTOXINS Chapter 1: Plant Toxins and Human Health, P S Spencer and F Berman, Oregon Health and Science University, USA Chapter 2: Bacterial Pathogens and Toxins in Foodborne Disease, E A Johnson, University of Wisconsin, USA Chapter 3: Shellfish Toxins, A Gago Martinez, University of Vigo, Spain and J F Lawrence, Health Canada, Ontario, Canada Chapter 4: Mycotoxins in Cereal Grains, Nuts and Other Plant Products, J P F D'Mello PART II: ANTHROPOGENIC CONTAMINANTS Chapter 5: Pesticides: Toxicology and Residues in Food and Wine, P Cabras, Universita di Cagliari, Italy Chapter 6: Polychlorinated Biphenyls, D L Arnold and M Feeley, Health Canada, Ottawa, Canada Chapter 7: Dioxins in Milk, Meat, Eggs and Fish, H Fiedler, UNEP, Geneva, Switzerland Chapter 8: Polycyclic Aromatic Hydrocarbons in Vegetable Oils, Seafood and Meat, M D Guillen and P Sopelana, Universiad del Pais Vasco, Vitoria, Spain Chapter 9: Heavy Metals, L Jorhem, National Food Administration,Uppsala, Sweden Chapter 10: Dietary Nitrates, Nitrites and N-nitroso Compounds and Cancer Risk with Special Emphasis on the Epidemiological Evidence, M Eichholzer and F Gutzwiller, University of Zurich, Switzerland Chapter 11: Adverse Reactions to Food Additives, R A Simon, Division of Allergy, Asthma and Immunology, Scripps Clinic, California, USA and H Ishiwata, National Institute of Health Sciences, Tokyo, Japan Chapter 12: Migration of Compounds from Food Contact Materials and Articles, J H Petersen, Danish Veterinary and Food Administration, Soborg, Denmark Chapter 13: Veterinary Products: Residues and Resistant Pathogens, J C Paige and L Tollefson, Division of Epidemiology, MD, USA PART III: CASE STUDIES Chapter 14: Prion Diseases: Meat Safety and Human Health Implications, N Hunter, Institute for Animal Health, Edinburgh, UK Chapter 15: The Safety Evaluation of Genetically Modified Foods, M J Gasson, Institute of Food Research, Norwich, UK Chapter 16: Genetically Modified Foods: Potential Human Health Effects, A Pusztai, S Bardocz, Aberdeen and S W B Ewen, University of Aberdeen, Scotland Chapter 17: Radionuclides in Food: The Post-Chernobyl Evidence, J T Smith and N A Beresford, Centre for Ecology and Hydrology, Dorchester, UK Chapter 18: Radionuclides in Foods: American Perspectives, E J Baratta, Winchester Engineering and Analytical Centre, MA, USA PART IV: CONCLUSIONS Chapter 19: Widespread and Continuing Concerns over Food Safety, J P F D'Mello
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.084 | 0.076 |
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".