Bibliographic record
Abstract
importance and value of embryonic phenotyping The International Mouse Phenotyping Consortium (IMPC; www.mousephenotype.org) aims to create 20,000 knockout (KO) mouse strains over the next 10 years, with viable strains undergoing comprehensive phenotyping as adult mice in order to identify the consequences of gene disruption. It is estimated that at least 30% of all KO strains will die during embryonic or perinatal periods and will not, therefore, pass through the adult phenotyping pipeline. However, systematic identification of such homozygous lethal KO lines presents the scientific community with a unique opportunity to study thousands of lethal phenotypes, unlocking a treasure trove of information relevant to gene function during embryonic growth, differentiation and organogenesis. This potential has been recognised by the mouse genetics community, as evidenced by previous IMPC workshops (Toronto, April 2010; Barcelona, February 2011), focus groups and user surveys in which embryonic development was considered an important stage that should be included in the IMPC pipeline (Brown and Moore, 2012). Identifying and characterising embryonic lethal mutant phenotypes is particularly important for understanding the roles of genes for which little to nothing is known. Embryonic lethal screens in model organisms, ranging in complexity from invertebrates to mammalian models, have to date proved extremely successful for the identification of genes and pathways that control developmental programmes. Recent case studies in the mouse include gene trapping (Cox et al., 2010) and chemical mutagenesis (Boles et al., 2009) screens covering proportions of the X chromosome and chromosome 11, respectively. These screens have demonstrated the power of forward genetic approaches for revealing functions of poorly annotated genes. For example, in the
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.268 | 0.109 |
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".