MetaMetaDB: A Database and Analytic System for Investigating Microbial Habitability
Bibliographic record
Abstract
MetaMetaDB (http://mmdb.aori.u-tokyo.ac.jp/) is a database and analytic system for investigating microbial habitability, i.e., how a prokaryotic group can inhabit different environments. The interaction between prokaryotes and the environment is a key issue in microbiology because distinct prokaryotic communities maintain distinct ecosystems. Because 16S ribosomal RNA (rRNA) sequences play pivotal roles in identifying prokaryotic species, a system that comprehensively links diverse environments to 16S rRNA sequences of the inhabitant prokaryotes is necessary for the systematic understanding of the microbial habitability. However, existing databases are biased to culturable prokaryotes and exhibit limitations in the comprehensiveness of the data because most prokaryotes are unculturable. Recently, metagenomic and 16S rRNA amplicon sequencing approaches have generated abundant 16S rRNA sequence data that encompass unculturable prokaryotes across diverse environments; however, these data are usually buried in large databases and are difficult to access. In this study, we developed MetaMetaDB (Meta-Metagenomic DataBase), which comprehensively and compactly covers 16S rRNA sequences retrieved from public datasets. Using MetaMetaDB, users can quickly generate hypotheses regarding the types of environments a prokaryotic group may be adapted to. We anticipate that MetaMetaDB will improve our understanding of the diversity and evolution of prokaryotes.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.008 |
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".