Gosha: a database of organisms with defined optimal growth temperatures
Bibliographic record
Abstract
ABSTRACT Currently, we are witnessing an explosive accumulation of genomic sequences for organisms across all branches of life. However, typically the genomic data lack the information about optimal growth conditions of corresponding organisms. As a result, it becomes challenging to use the genomic data for studying the adaptations of organisms and biological molecules to diverse environments. To address this problem, we have created a database Gosha, available at http://melnikovlab.com/gshc . This database brings together information about the genomic sequences and optimal growth temperatures for 25,324 species, including ∼89% of the bacterial species with known genome sequences. Using this database, one can annotate genomic sequences from thousands of species and correlate variations in genes and genomes with optimal growth temperatures. The database interface allows users to retrieve optimal growth temperatures for bacteria, eukaryotes and archaea, providing a tool to explore how organisms, genomes, and individual proteins and nucleic acids adapt to certain temperatures. We hope that this database will contribute to medicine and biotechnology by helping to create a better understanding of molecular adaptations to heat and cold, leading to new ways to preserve biological samples, engineer useful enzymes, and develop biological materials and organisms with the desired tolerance to heat and cold. GRAPHICAL ABSTRACT Gosha (available at www.melnikovlab.com/gshc ) is a database that collects information about the optimal growth temperatures of living species. This database aims to facilitate studies of molecular adaptation to specific temperatures. Abstract Figure
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.042 | 0.031 |
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".