Health Canada: Current Topics in Food Chemical Safety Research
Bibliographic record
Abstract
This special issue of Food Additives and Contaminants dedicated to ‘Health Canada: Current Topics in Food Chemical Safety Research’ will be the first published document where the focus is on Health Canada's food research activities. This issue features a selection of papers arising from work in the Food Research Division of the Bureau of Chemical Safety within Health Canada's Food Directorate and their partners in the Health Canada's Regional Laboratories, in collaboration with international partners. The papers covered in this issue deal with the determination of chemicals, such as brominated flame retardants, monochloropropanediols, melamine, bisphenol A, perchlorate and trace metals, but also with mycotoxins, phycotoxins and allergens that are introduced to foods either as a result of their occurrence in the environment, natural infection by fungi, food processing, food packaging or other human activities. To deal with the increasing number of sample matrices and contaminants of interest, fast and accurate analytical methods are needed. This demand has led to the development of rapid screening methods for various analytes based on immunochemical techniques, including the use of surface plasmon resonance. Moreover, highly sophisticated multi-analyte methods based on liquid chromatography coupled with multiple-stage mass spectrometry have been developed to allow identification and simultaneous determination of a wide range of contaminants, and often with much less requirement for tedious clean-up procedures. Thanks to the contributors, to whom we would like to express our great gratitude, recent developments in all the areas mentioned above can be presented in this issue. It is hoped that the collection of these papers stimulates further research the better to protect consumers through improved methods for the sensitive and accurate determination of contaminants in foods, which will also lead to improved exposure estimates and risk assessments. We are also very thankful to the Editor-in-Chief of the journal, John Gilbert, for his support and fantastic cooperation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".