Genotype to Phenotype: Design of an Extensible Experimental Platform for Characterizing Microbes
Bibliographic record
Abstract
Currently, there are no predictive models of microbial growth under various conditions that utilize data generated specifically for machine learning applications, and that are collected under consistent experimental conditions. This project aims to develop an extensible experimental platform and standardized data ontology for collecting phenotypic measurements of microbes grown in various cultivation conditions. Our goal is to understand and predict how environmental conditions interact with microbial genotypes to affect phenotypes such as growth and function. To do this, we will generate a comprehensive, machine learning-ready dataset comprising growth data for 1,000 culturable microbial strains across 1,000 cultivation conditions, resulting in one million unique experiments. Each strain will be cultured under controlled conditions, and growth measurements will be systematically collected along with a suite of additional phenotypic assays. This dataset will provide a deeper understanding of microbial growth dynamics and phenotypes across diverse environmental conditions, ultimately enabling the development of robust predictive models
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".