First dark matter search results from a 4-kg<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:msub><mml:mi>CF</mml:mi><mml:mn>3</mml:mn></mml:msub><mml:mi mathvariant="bold">I</mml:mi></mml:math>bubble chamber operated in a deep underground site
Bibliographic record
Abstract
New data are reported from the operation of a 4.0-kg ${\mathrm{CF}}_{3}\mathrm{I}$ bubble chamber in the 6800-foot-deep SNOLAB underground laboratory. The effectiveness of ultrasound analysis in discriminating alpha-decay background events from single nuclear recoils has been confirmed, with a lower bound of $>99.3%$ rejection of alpha-decay events. Twenty single nuclear recoil event candidates and three multiple bubble events were observed during a total exposure of 553 kg-days distributed over three different bubble nucleation thresholds. The effective exposure for single bubble recoil-like events was 437.4 kg-days. A neutron background internal to the apparatus, of known origin, is estimated to account for five single nuclear recoil events and is consistent with the observed rate of multiple bubble events. The remaining excess of single bubble events exhibits characteristics indicating the presence of an additional background. These data provide new direct detection constraints on WIMP-proton spin-dependent scattering for WIMP masses $>20\text{ }\text{ }\mathrm{GeV}/{\mathrm{c}}^{2}$ and demonstrate significant sensitivity for spin-independent interactions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".