Functional foods for health promotion: microbes and health Extended abstracts from the 11th Annual Conference on Functional Foods for Health Promotion, April 2008
Bibliographic record
Abstract
The extended abstracts in this report are based on presentations from the 11th Special Conference on Functional Foods for Health Promotion, cosponsored by the Technical Committee on Food Components for Health Promotion of the North American Branch of the International Life Sciences Institute (ILSI NA) and the American Society for Nutrition (ASN) at the Experimental Biology (EB) meeting in April 2008. Evidence that foods and their components offer health benefits beyond basic nutrition continues to captivate the interest of scientific communities, federal agencies, and the general public. The theme of the 2008 special conference was “Microbes and Health”. It began with a general introduction and overview of how diet or dietary components can influence microbial growth and, ultimately, disease risk and overall health. Subsequent presentations provided fundamental information about how the food supply can set the landscape for gene expression in microbes and, ultimately, their influence on health (with some comments on how microbes might contribute to the obesity epidemic), child health and infections, role of microbes in cancer prevention, and effects of foods and their bioactive constituents as modifiers of microbes in the oral cavity.
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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.048 | 0.019 |
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".