Bibliographic record
Abstract
The human colon is an anaerobic environment densely populated with bacterial species, creating what is known as the human intestinal microbiome; an ecosystem imperative to physiological function with regards to metabolism of non-digestible residues, growth of cells and immune protection from invading organisms. As such, quantifying, and subsequently developing an understanding of the behaviour of this microbial population can be of great value. Unfortunately, because of the physical inaccessibility of many parts of the gastro-intestinal (GI) tract, routine experimentation with this environment is not practical. However, theoretical modelling techniques including in vitro and in silico simulation/experimental platforms provide a means by which further studying of intestinal microflora can be approached. Perfecting these theoretical models is an important step in further understanding colon microbiota. An existing in silico model of carbohydrate digestion in the colon, developed by Munoz-Tamayo et al. (2010) has been used as a platform for experimentation with the intention of of discovering features which may be removed and/or added to improve the performance and reliability of the design. The model is an adaptation of the waste-water engineering based mathematical model ADM1 (Anaerobic Digestion Model 1), developed to incorporate biochemical and environmental specifications as well as physical structures particular to the human colon. The model is then a system of 102-ordinary differential equation with 66 parameters.Simulations with the default model configuration as well as variations of input variables, namely dietary fiber consumption and system flow rate, were completed to study the effect on average biomass concentration, demonstrating significant sensitivity to input variables and an unexpected linearity based on the non-linearity of the original complex system. Simulations and further study suggest that advancements in in silico modelling of the colon rely on the development of a metric or scheme that can effectively compare mathematically generated data with that collected through traditional experimentation. Also, experimenting with various reactor configurations as a basis for mathematical modelling may prove simpler configurations capable of generating comparable data to more complicated designs which may then also be applicable to existing in vitro representations of the colon.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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 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".