What does 1.0 take? MISO LIMS after 9 years of development
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
MISO is a laboratory information management system designed for eukaryotic sequencing operations. It supports genomic, exomic, transcriptomic, methyl-omic, and CHiP-seq protocols; long reads and short reads; and microarrays. MISO’s goals are to allow laboratory technicians to record their work accurately with a minimum of data entry overhead, and to ensure the associated metadata is valid and structured enough to use for automation and other downstream applications. MISO incorporates a wide feature set useful for both large and small facilities to track their lab workflows in great detail. Since last presented at BOSC 2016, MISO has matured and stabilized to support production use in a large sequencing facility. MISO supports new instruments like the Illumina NovaSeq, 10X Chromium, and Oxford Nanopore PromethION, added more extensive location tracking, improved UI interfaces to simplify data entry, has improved overall performance, and has extensive documentation in the form of a new user manual and walkthroughs. Recently we have improved installation, administration, and maintenance through Docker containers and compose files. We have developed other applications that interact with MISO to facilitate laboratory functions like billing, reporting, and analysis. After 8 years of development, we are preparing a 1.0 release for late 2019.<br>
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 | 0.000 | 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) | 0.001 | 0.001 |
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