Bibliographic record
Abstract
thank you very much for inviting me. It is great to be here in Prague. It is my first visit to Prague. And I must say I am enjoying the city. Slide # 1: Topic introduction What I am going to talk about is a new concept called the “metagenome.” As we understand more about the human genome and the pathogenic genomes we are starting to understand more about how the various pathogens — be they Chlamydia, Mycobacteria, and Mycoplasma — how the various pathogens interact with our own genome in order to cause disease. And so my title is based on “Infectious Disease transitions to an understanding of the Metagenome.” Slide # 2: In vivo, in vitro, in silico There are three types of biology that are pretty common these days. The first type, in vivo, of course, in animal or human models. In vitro is where a lot of the work on antibiotics is being done; in cell culture, in the lab. In silico is very new. The first time I came across in silico was at this gathering here in Toronto back in 1981. Human insulin had just been synthesized using mathematical formulae, using the IBM supercomputer that could simulate the insulin molecule at the level of the mathematics. And that [in silico] is really what I have been doing over the last decade. Slide # 3: The NIH Human Microbiome Project There is a new push going on at the moment. The NIH in the USA has started the Microbiome Project. The goal of the Microbiome Project is to characterize all of the places in the human body where genomes, other than the human genome, are also present. And NIH has estimated that about 10 % of the cells in the body are human cells, and about 90 % of the cells in a normal healthy individual’s body are bacterial cells. Now, remember that bacteria cells are very, very small. And in most cases the bacterial cells — many, many hundreds of bacterial cells — can live within infected human cells. But we are starting to get to an understanding now that the It is time to bury Koch — Infectious disease transitions to an understanding of the
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.680 | 0.629 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".