Bibliographic record
Abstract
Abstract Summary Delivering tools for genome analysis to users is often difficult given their complex dependencies and conflicts. Container virtualization systems such as Singularity isolate environments, helping developers avoid conflicts between tools. However, they lack composability , an easy way to integrate multiple tools in different containers or multiple tools both in a container and a host, which compromises the use of container systems in genome research. Another issue is that one may not be able to use a single container system of the same version at all sites they use, which discourages the use of container systems. To this end, we present a pure rootless composable container system, LPMX, that provides composability for letting developers easily integrate tools in different existing containers or on host, allowing researchers to compose existing containers. LPMX is pure rootless, so it does not require root privilege neither during installation nor at runtime, allowing researchers to use LPMX across sites without asking permissions from administrators. LPMX provides a pure userspace layered filesystem with at least an order of magnitude lower overhead for launching a new process than existing container systems. LPMX can import Docker and Singularity images. Availability and Implementation The source code of LPMX is available at https://github.com/jasonyangshadow/lpmx under Apache 2.0 License. Contact mkasa@k.u-tokyo.ac.jp Supplementary information Supplementary data are available at Bioinformatics online.
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.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.010 |
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".