The Science of the Deep Underground Neutrino Experiment (DUNE)
Bibliographic record
Abstract
This 4-minute animation shows how the international Deep Underground Neutrino Experiment will help scientists understand how the universe works. DUNE will use a huge particle detector a mile underground to embark on a mission with three major science goals: 1.) Study an intense, 1,300-kilometer-long neutrino beam to discover what happened after the big bang: Are neutrinos the reason the universe is made of matter? 2.) Use 70,000 tons of liquid argon to look for proton decay and move closer to realizing Einstein’s dream of a unified theory of matter and energy. 3.) Catch neutrinos from a supernova to watch the formation of neutron stars and black holes in real time. About 1,000 scientists from 160 institutions in 30 countries are working on the Deep Underground Neutrino Experiment, hosted at the Department of Energy’s Fermi National Accelerator Laboratory and South Dakota’s Sanford Underground Research Facility. DUNE collaborators come from institutions in Armenia, Brazil, Bulgaria, Canada, Chile, China, Colombia, Czech Republic, Finland, France, Greece, India, Iran, Italy, Japan, Madagascar, Mexico, Netherlands, Peru, Poland, Romania, Russia, South Korea, Spain, Sweden, Switzerland, Turkey, Ukraine, United Kingdom, and the United States of America.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.057 | 0.013 |
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".