Bibliographic record
Abstract
\nThe Deep Underground Neutrino Experiment (DUNE) has so far represented data using a combination of custom data formats and those based on ROOT I/O. Recently, DUNE has begun using the Hierarchical Data Format (HDF5) for some of its data storage applications. HDF5 provides high-performance, low-overhead I/O in DUNE’s data acquisition (DAQ) environment. DUNE will use HDF5 to record raw data from the ProtoDUNE-II Horizontal Drift (HD), ProtoDUNE-II Vertical Drift (VD) and a number of test stands. Dedicated I/O modules have been developed to read the HDF5 data from these detectors into the offline framework for reconstruction directly and via XRootD. HDF5 is also very commonly used on High Performance Computers (HPCs) and is well-suited for use in AI/ML applications. The DUNE software stack contains modules that export data from an offline job in HDF5 format, so that they can be processed by external AI/ML software. The collaboration is also developing strategies to incorporate HDF5 in the detector simulation chains.\n
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.005 |
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".