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Record W4312141125 · doi:10.3390/foods11244099

Food Toxicology and Food Safety: Report of the 3rd International Electronic Conference on Foods: Food, Microbiome, and Health—A Celebration of the 10th Anniversary of Foods’ Impact on Our Wellbeing

2022· article· en· W4312141125 on OpenAlexfundno aff
Dirk W. Lachenmeier, Paula A. Oliveira, Agata Fabiszewska, Cristina Soares, Jong H. Kim

Bibliographic record

VenueFoods · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicGABA and Rice Research
Canadian institutionsnot available
FundersBundesanstalt für Landwirtschaft und ErnährungInstitut National de la Recherche AgronomiqueMinistério da Ciência, Tecnologia e Ensino SuperiorJoint Programming Initiative A healthy diet for a healthy lifeFonds Wetenschappelijk OnderzoekMinistero delle Politiche Agricole Alimentari e ForestaliRéseau de cancérologie Rossy
KeywordsFood safetyEnvironmental healthMedicineAgricultureMicrobiomeBiotechnologyFood scienceBiology

Abstract

fetched live from OpenAlex

The purpose of the conference session summarized in this article was to bring together international experts on food toxicology and food safety and share the current scientific knowledge on these topics. The presentations covered a wide range of interdisciplinary issues, including (i) the impact of diet on body weight and health outcomes including results from animal models of carcinogenesis, (ii) methods for microbial oil extraction, (iii) food processing and its impact on food safety and health, (iv) novel compounds to avoid mycotoxin contamination of agricultural products, and (v) the safety of cannabidiol in food supplements based on Cannabis sativa extracts. Some of the conclusions of the presentations included that correct food choices may impact on the risk of non-communicable diseases such as cancer, that food processing may have an influence on health, by either reducing or increasing risks, and that research regarding novel compounds is important, which may have preventive but also detrimental effects on health.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.873
Threshold uncertainty score0.218

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.290
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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