A New Era of Discovery: The 2023 Long-Range Plan for Nuclear Science (V.1.2)
Bibliographic record
Abstract
Nuclear science is the investigation of how protons and neutrons are formed from elementary particles and how the forces between those particles produce both nuclei and the vast variety of nuclear phenomena that occur in the universe. It has evolved into a broad field that addresses profound scientific questions: Where does the mass of visible matter come from? How do stars ignite, live, and die? How do nuclei illuminate the search for new laws of nature? This science points the way to using nuclei to build new technologies that benefit society. The 2015 Nobel Prize in physics was shared by nuclear physicists Art McDonald and Takaaki Kajita for the discovery of neutrino oscillations, which confirmed that neutrinos have mass. Our progress on big questions like this one since 2015 has been remarkable owing to new experimental tools, theoretical breakthroughs, powerful computational techniques, and the talented people who make these innovations possible. Focusing on these new tools, the Facility for Rare Isotope Beams (FRIB) at Michigan State University is already producing exciting results on decays of never-before-produced isotopes a year after it was completed on time and on budget. The energy upgrade of the Continuous Electron Beam Accelerator Facility (CEBAF) at the Thomas Jefferson National Accelerator Facility (Jefferson Lab) was also completed on schedule and on budget—new data from this facility are revealing the spectrum, structure, and dynamics of protons, neutrons, nuclei, and mesons. On the theory front, we can now calculate the distribution of quarks inside the proton from first principles. The implementation of artificial intelligence (AI) and machine learning (ML) techniques has led to improved data analysis and increased efficiency in running experiments and theoretical calculations. The impact of nuclear science goes beyond expanding the frontiers of knowledge about matter in the universe. We simultaneously develop a STEM work force that advances the security, technology, health, and wealth of our nation. Some connections are obvious. Expert scientists trained to work with radioactive nuclei are in demand in nuclear security arenas and are highly sought after by various government agencies and private industries. Graduate students and postdoctoral fellows (postdocs) obtain extensive computational, modeling, and data science skills that are similarly in high demand. Less obvious but equally important is the connection between these trained scientists and success in other professions, including medicine, energy, and entrepreneurial pursuits. The workforce that enables discovery in nuclear science also makes breakthroughs in technologies with tremendous impact on the nation’s economic advancement.
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.
How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".