{"id":"W2905828428","doi":"10.1103/physrevd.99.083010","title":"Foraging for dark matter in large volume liquid scintillator neutrino detectors with multiscatter events","year":2019,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":50,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria; Queen's University; TRIUMF; Arthur B. McDonald-Canadian Astroparticle Physics Research Institute; Perimeter Institute","funders":"Natural Sciences and Engineering Research Council of Canada; Institut Périmètre de physique théorique; National Research Council Canada; Industry Canada; Ontario Ministry of Economic Development and Innovation; National Science Foundation; Government of Canada; Aspen Center for Physics; TRIUMF; Ministero dello Sviluppo Economico; U.S. Department of Energy","keywords":"Physics; Dark matter; Neutrino; Particle physics; Nuclear physics; Scintillator; Sensitivity (control systems); Detector; Optics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007557221,0.0004522538,0.0003320501,0.0005621843,0.000292548,0.001060212,0.0008169226,0.0004997703,0.002668765],"category_scores_gemma":[0.001728644,0.000313182,0.0004165219,0.000688886,0.0003700303,0.0008752772,0.0006365305,0.000286437,0.0003549097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000937621,"about_ca_system_score_gemma":0.0003713764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001230499,"about_ca_topic_score_gemma":0.00245017,"domain_scores_codex":[0.9997233,0.00007434301,0.00001057625,0.00006044268,0.00008568235,0.00004569891],"domain_scores_gemma":[0.9989349,0.0005781598,0.0002693361,0.00008565717,0.00008336786,0.00004857878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001777008,0.0002754344,0.09413498,0.0006921222,0.0005074473,0.004253421,0.0005099276,0.1933886,0.09920701,0.4818165,0.006133854,0.1173036],"study_design_scores_gemma":[0.0002901076,0.0003655684,0.02433036,0.0001174429,0.000398146,0.001687445,0.000161788,0.7499917,0.1023738,0.1069772,0.01320656,0.00009978866],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7576585,0.003712249,0.2016332,0.0006890146,0.0001014663,0.00006699218,0.0004914766,0.001305253,0.03434191],"genre_scores_gemma":[0.9840298,0.0005400923,0.01317077,0.00008104339,0.00002056994,0.00002689515,0.0002447037,0.000051477,0.001834654],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002668765,"threshold_uncertainty_score":0.008927941,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007180025565078022,"score_gpt":0.3416806920568171,"score_spread":0.3345006664917391,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}