Introduction to the Japan Mouse Clinic at the RIKEN BioResource Center
Bibliographic record
Abstract
A systematic and comprehensive phenotyping platform has been developed by the RIKEN ENU-mutagenesis project between 1999 and 2007. As a result of phenotype screening on this platform, we have discovered about 400 mutants as animal models for human diseases. All information regarding these mouse mutants is now available to the public through our home page (http://www.brc.riken.jp/lab/gsc/mouse/indexJ.html). In 2008, we reconstructed the existing phenotyping platform and built a new platform. The new system has a hierarchical structure, consisting of a fundamental pipeline that utilizes the existing platform and an additional pipeline, which is optimized for more in-depth phenotyping assays. Using this system, we have started to perform more comprehensive phenotyping of mouse mutants. We have opened this system to Japanese scientists as the Japanese Mouse Clinic. It is anticipated that existing mouse mutants will be reevaluated as disease models by identifying novel phenotypes on the new platform. We will share detailed information about the standard operating procedures (SOPs) of our phenotyping analyses with other related large-scale projects, such as the European Mouse Disease Clinic (EUMODIC) and the German Mouse Clinic (GMC). Moreover, we will contribute to international efforts to standardize mouse phenotype data by sharing annotation of mutant phenotypes, which are made by internationally standardized methods, with other related projects.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 0.019 |
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".