Cryocooled cold trap system for the SuperCDMS dilution refrigerator
Bibliographic record
Abstract
Abstract Operating 6,800 feet underground at the SNOLAB facility in Sudbury, Ontario, Canada, the dilution refrigerator-cooled SuperCDMS SNOLAB (Super Cryogenic Dark Matter Search at the Sudbury Neutrino Observatory Laboratory) experiment has been designed for maximum cryogenic up-time and remote operations. A key element in achieving these goals is a pair cold traps in the helium circulation stream of the dilution refrigerator; the first operating near liquid nitrogen temperatures and the second operating near liquid helium temperatures. Previous experience with the CDMS experiment, located underground at the Soudan Under-ground Laboratory, has given significant operational experience with dilution refrigerator cold traps and has solidified the demand of a system of dual cold traps. Unlike the CDMS-era system, the new SuperCDMS system will feature a cryocooler powered liquid nitrogen re-liquefying system (as opposed to regular under-ground re-filling of cold trap dewars using portable nitrogen dewars) and a cryogen-free 4 K cold trap, which eliminates the need for a bath of liquid helium.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".