The Science of the Deep Underground Neutrino Experiment (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
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.
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 | 0.001 |
| Science and technology studies | 0.000 | 0.005 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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