Metagenomic Thermometer
Bibliographic record
Abstract
Abstract Various microorganisms exist in environments, and each of which has an optimal growth temperature (OGT). The relationship between genomic information and OGT of each species has long been studied, and one such study revealed that OGT of prokaryotes can be accurately predicted based on the fraction of seven amino acids (IVYWREL) among all encoded amino-acid sequences in its genome. Extending this discovery, the fraction of IVYWREL among all amino acids encoded in the metagenomic DNA in an environment may reflect the environmental temperature to which the microorganisms have adapted. In the present study, we developed a ‘Metagenomic Thermometer’ as a means of predicting environmental temperature based on metagenomic sequences. Analysis of 41 metagenomic samples obtained from hot springs of varying temperature revealed that the Metagenomic Thermometer could indeed predict environmental temperature with high accuracy ( R 2 = 0.75). Although the taxonomic composition of microbial community was substantially affected by pH, the performance of the Metagenomic Thermometer was refractory to changes in community composition. The Metagenomic Thermometer was also applied to 616 human gut metagenomic samples, yielding a reasonably accurate value (i.e., 36.22 ± 1.98°C) for human body temperature. Our results suggest that environmental temperature can be predicted based on metagenomic sequence data.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".