ORCA: a comprehensive bioinformatics container environment for education and research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SUMMARY: The ORCA bioinformatics environment is a Docker image that contains hundreds of bioinformatics tools and their dependencies. The ORCA image and accompanying server infrastructure provide a comprehensive bioinformatics environment for education and research. The ORCA environment on a server is implemented using Docker containers, but without requiring users to interact directly with Docker, suitable for novices who may not yet have familiarity with managing containers. ORCA has been used successfully to provide a private bioinformatics environment to external collaborators at a large genome institute, for teaching an undergraduate class on bioinformatics targeted at biologists, and to provide a ready-to-go bioinformatics suite for a hackathon. Using ORCA eliminates time that would be spent debugging software installation issues, so that time may be better spent on education and research. AVAILABILITY AND IMPLEMENTATION: The ORCA Docker image is available at https://hub.docker.com/r/bcgsc/orca/. The source code of ORCA is available at https://github.com/bcgsc/orca under the MIT license.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it