Bibliographic record
Abstract
MiniCLEAN-360 utilizes 360 kg of liquid argon to detect the nuclear recoil from WIMP dark matter with a projected cross-section sensitivity of 10 −45 cm 2 . To reach this planned sensitivity, a unique modular design is being developed with a spherical geometry to maximize light collection using PMTs. Pulse shape discrimination techniques separate nuclear recoil signal from the electron recoil backgrounds resulting from the beta decay of 39 Ar and Compton scattering of gamma rays. The design allows for the replacement of the target material with liquid neon to examine any signal and backgrounds with a different sensitivity. It also provides research and development for a larger scale low energy solar neutrino experiment using neon (CLEAN: Cryogenic Low Energy Astrophysics with Noble gases) that plans to measure the pp-solar neutrino flux to 1%. Particular attention is being paid to mitigating the background from contamination of surfaces by radon daughters during assembly. The engineering design, radon mitigation plan, and various testing setups are presented. MiniCLEAN-360 anticipates the start of data collection in mid-2009 at SNOLAB in Sudbury, Ontario, Canada.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".