FORM, a Fine-grained Object Reading/Writing Model for DUNE
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
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.
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) | 0.000 | 0.000 |
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