Bibliographic record
Abstract
Investigations at the frontiers of particle physics are driving key experiments deep underground to escape cosmic-rays and other backgrounds that overwhelm sensitive searches for dark matter, exotic particles, and neutrino properties. The international neutrino physics community has come together to develop the Deep Underground Neutrino Experiment (DUNE), a leading-edge experiment for neutrino science and proton decay studies. This experiment, together with the facility that will support it, the Long Baseline Neutrino Facility (LBNF), will be an internationally designed, coordinated and funded program, hosted at Fermilab in Batavia, Illinois. The DUNE experimental facility will be sited deep underground (1.5km) at Sanford Underground Research Facility (SURF) in Lead, SD, and will become the deepest experimental facility operated by Fermilab. SNOLAB is a unique world-class international facility for deep underground scientific research. Located 2 km underground near Sudbury, Ontario, SNOLAB hosts a suite of surface facilities and laboratories. The science program at SNOLAB is primarily focused on subatomic and astroparticle physics, specifically the search for dark matter and neutrino studies. As the second-deepest underground lab facility, SNOLAB has gained considerable experience in deep underground scientific research operations. The purpose of this agreement is to leverage the unique capabilities of both Fermilab and SNOLAB to accomplish the following: Provide Fermilab with additional, direct experience in deep underground scientific research operations at SNOLAB to help improve the operational support for SURF. Provide SNOLAB with an experienced leader to serve as Interim Associate Director for Program Integration & Operations, while SNOLAB conducts its search for a permanent hire.
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 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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.280 | 0.223 |
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".