Empowering medaka fish biology with versatile genomic resources in MedakaBase
Bibliographic record
Abstract
Abstract Medaka, a group of small, mostly freshwater fishes in the teleost order Beloniformes, includes the rice fish Oryzias latipes , which is a prominent model organism for diverse biological fields. Chromosome-scale genome sequences of the Hd-rR strain of this species were obtained in 2007, and its improved version has facilitated various genome-wide studies. However, despite its widespread utility, omics data for O. latipes are dispersed across various public databases and lack a centralized platform. To address this, the medaka section of the National Bioresource Project (NBRP) of Japan established a genome informatics team in 2022 tasked with providing versatile in silico solutions for bench biologists. This initiative led to the launch of MedakaBase ( https://medakabase.nbrp.jp ), a web server that enables gene-oriented analysis including exhaustive sequence similarity searches. MedakaBase also provides genome-wide browsing of diverse datasets, including tissue-specific transcriptomes and intraspecific genomic variations, integrated with gene models from different sources. Additionally, the platform offers gene models optimized for single-cell transcriptome analysis, which often requires coverage of the 3′ untranslated region (UTR) of transcripts. Currently, MedakaBase provides genome-wide data for seven Oryzias species, including original data for O. mekongensis and O. luzonensis produced by the NBRP team. This article outlines technical details behind the data provided by MedakaBase.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.018 |
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".