{"id":"W3067687984","doi":"10.1103/physrevd.103.075002","title":"Silicon carbide detectors for sub-GeV dark matter","year":2021,"lang":"en","type":"article","venue":"Physical review. D/Physical review. D.","topic":"Dark Matter and Cosmic Phenomena","field":"Physics and Astronomy","cited_by":95,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Lawrence Berkeley National Laboratory; Laboratory Directed Research and Development; Office of Science; Israeli Centers for Research Excellence; Azrieli Foundation; Israel Science Foundation; United States-Israel Binational Science Foundation; Alfred P. Sloan Foundation; Basic Energy Sciences; National Energy Research Scientific Computing Center; German-Israeli Foundation for Scientific Research and Development; Fermilab; High Energy Physics; U.S. Department of Energy","keywords":"Dark matter; Silicon carbide; Diamond; Silicon; Detector; Absorption (acoustics); Scattering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004408777,0.0005397334,0.0003117652,0.0008948164,0.0002085857,0.0007919076,0.0008728424,0.001136602,0.001957701],"category_scores_gemma":[0.000396417,0.0001923419,0.0003231529,0.0003637126,0.000577571,0.0005483441,0.0005150035,0.001052682,0.001203725],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004758668,"about_ca_system_score_gemma":0.0004236858,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006523964,"about_ca_topic_score_gemma":0.001277393,"domain_scores_codex":[0.9997429,0.00003778197,0.000007074396,0.00005132844,0.0001357853,0.00002516278],"domain_scores_gemma":[0.9998171,0.00004776672,0.00002731495,0.00001652126,0.00007527555,0.00001607663],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003329983,0.000178114,0.01000066,0.002958505,0.0002519494,0.001175907,0.0001157487,0.002114606,0.3389882,0.1726958,0.0458336,0.4253541],"study_design_scores_gemma":[0.000102048,0.0005869498,0.006774102,0.0005137096,0.0002119885,0.003093548,0.0001070152,0.007555572,0.270435,0.04101061,0.6695237,0.0000858842],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.08809222,0.6142289,0.08140758,0.009234007,0.003972562,0.0003329171,0.0006299733,0.0007888497,0.201313],"genre_scores_gemma":[0.5961369,0.2701382,0.08073349,0.004462994,0.001746727,0.0002464728,0.0008207906,0.00007609697,0.04563838],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001957701,"threshold_uncertainty_score":0.00654918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01223089812540847,"score_gpt":0.3667669098882725,"score_spread":0.3545360117628641,"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."}}