First data from DM-Ice17
Bibliographic record
Abstract
We report the first analysis of background data from DM-Ice17, a direct-detection dark matter experiment consisting of 17 kg of NaI(Tl) target material. It was codeployed with IceCube 2457 m deep in the South Pole glacial ice in December 2010 and is the first such detector operating in the Southern Hemisphere. The background rate in the $6.5--8.0\text{ }\text{ }{\mathrm{keV}}_{\mathrm{ee}}$ region is measured to be $7.9\ifmmode\pm\else\textpm\fi{}0.4\text{ }\text{ }\text{counts}/\text{day}/\mathrm{keV}/\mathrm{kg}$. This is consistent with the expected background from the detector assemblies with negligible contributions from the surrounding ice. The successful deployment and operation of DM-Ice17 establishes the South Pole ice as a viable location for future underground, low-background experiments in the Southern Hemisphere. The detector assembly and deployment are described here, as well as the analysis of the DM-Ice17 backgrounds based on data from the first two years of operation after commissioning, July 2011--June 2013.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".