A Canadian Working Group report on fecal microbial therapy: microbial ecosystems therapeutics.
Bibliographic record
Abstract
Africa has an age old history of production of traditional fermented foods and is perhaps the continent with the richest variety of lactic acid fermented foods. These foods have a large impact on the nutrition, health and socio-economy of the people of the continent, often plagued by war, drought, famine and disease. Sub-Saharan Africa is the world's region with the highest percentage of chronically malnourished people and high child mortality. Further developing of traditional fermented foods with added probiotic health features would be an important contribution towards reaching the UN Millennium Development Goals of eradication of poverty and hunger, reduction in child mortality rates and improvement of maternal health. Specific probiotic strains with documented health benefits are sparsely available in Africa and not affordable to the majority of the population. Furthermore, they are not used in food fermentations. If such probiotic products could be developed especially for household food preparation, such as cereal or milk foods, it could make a profound impact on the health and well-being of adults and children. Suitable strains need to be chosen and efforts are needed to produce strains to make products which will be available for clinical studies. This can gauge the impact of probiotics on consumers' nutrition and health, and increase the number of people who can benefit.
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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.010 |
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".