Creation and Use of a Cre Recombinase Transgenic Database
Bibliographic record
Abstract
In many cases, a gene "knockout" results in early embryonic lethality, which obscures the study of potential later functions. In other cases, the "knockout" does not show any phenotype due to the compensation of the gene deficiency by other family members. These limitations have called for further development of the powerful gene-targeting technology. One of the critical tools now being efficiently combined with gene-targeting is site-specific recombination. As the site-specific recombinase technology developed further in the mouse system, it became evident that this tool was going to have a significant impact on the power of mammalian genetics. The number of transgenic mouse lines expressing Cre recombinase with different specificities has steadily increased in the past 15 years and has now surpassed 500. Efficient utilization of this community-generated resource calls for a user-friendly database with all necessary information available about the properties of the Cre transgenic lines. The "CreXmice" database was created to meet these needs and has evolved over the past 4 years from flat file listings of transgenic lines into its current form, a professionally hosted SQL-driven web application. With hundreds of transgenic mouse lines, CreXmice is enriched by its presence on the World Wide Web allowing visitors the opportunity to search or contribute to the global effort by submitting the specific lines being developed by their laboratories.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.026 |
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".