Infrafrontier - Mouse models and phenotyping data for the European biomedical research community
Bibliographic record
Abstract
It is clear that this tremendous task cannot be fulfilled by individual research facilities or on the national level alone. This is the rationale for the European project Infrafrontier (The European infrastructure for phenotyping and archiving of model mammalian genomes, www.infrafrontier.eu ), which is coordinated at the Helmholtz Zentrum Munchen by Prof. Hrabe de Angelis. Infrafrontier is on the European roadmap for research infrastructures of ESFRI (European Strategy Forum for Research Infrastructures, http://www.cordis.europa.eu/esfri ) and receives funding from the EC’s Seventh Framework Program. It will organise a pan-European research infrastructure to increase the capacities for systemic phenotyping and archiving of mouse models. The Infrafrontier consortium currently contains 22 partners (representing 14 phenotyping and archiving centres, 1 bioinformatics institute and 12 European ministries and funding agencies) from 10 different European countries. Six new partners will join Infrafrontier in the near future, extending the project to Austria, Czech Republic and Canada.
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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 | 0.023 | 0.022 |
| 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 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.037 | 0.035 |
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".