Human Analysts at Superhuman Scales: What Has Friendly Software To Do?
Bibliographic record
Abstract
As analysts are expected to process a greater amount of information in a shorter amount of time, creators of big data software are challenged with the need for improved efficiency. Ray, our group's usable, scalable genome assembler, addresses big data problems by using optimal resources and producing one, correct and conservative, timely solution. Only by abstracting the size of the data from both the computers and the humans can the real scientific question, often complex in itself, eventually be solved. To draw a curtain over the specific computational machinery of big data, we developed RayPlatform, a programming framework that allows users to concentrate on their domain-specific problems. RayPlatform is a parallel message-passing software framework that runs on clouds, supercomputers, and desktops alike. Using established technologies such as C++ and MPI (message-passing interface), we handle the genomes of hundreds of species, from viruses to plants, using machines ranging from desktop computers to supercomputers. From this experience, we present insights on making computer time more useful-and user time much more valuable.
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 | 0.050 | 0.131 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.005 | 0.015 |
| Scholarly communication | 0.013 | 0.027 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".