FORM, a Fine-grained Object Reading/Writing Model for DUNE
Bibliographic record
Abstract
DUNE’s current processing framework (art) was branched from the event processing framework of CMS, a collider-physics experiment. Therefore art is built on event-based concepts as its fundamental processing unit. The “event” concept is not always helpful for neutrino experiments, such as DUNE. In DUNE, each event is represented by a trigger record, which can be much larger than a typical collider event — often several gigabytes, compared to just megabytes for collider events. To avoid allocating large chunks of memory due to the large and complex nature of DUNE’s events, the experiment is developing a framework (Phlex) that is able to break apart trigger records into smaller segments for more granular processing, and then stitch those chunks back together into an event. For an event-processing framework to function efficiently, it must be integrated with an input/output (I/O) system that supports fine-grained data handling. FORM (Fine-grained Object Reading/Writing Model) is a DUNE project focused on developing a data storage and I/O system that enables information to be written and accessed in smaller, more manageable units supporting framework that perform fine-grained event processing. To support fine-grained processing, data objects are partitioned into segments and stored separately in accessible locations. This approach allows the I/O system to read and write individual segments, avoiding the high memory usage that comes from handling large monolithic data objects. The complexity of data storage and I/O operations is encapsulated within the FORM infrastructure, making it transparent to client-side components like processing algorithms. By writing and reading multiple smaller entries as discrete events, FORM improves concurrency and scalability in the data processing pipeline.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".