{"id":"W2080224060","doi":"10.1103/physrevlett.104.160501","title":"Testing Contextuality on Quantum Ensembles with One Clean Qubit","year":2010,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Mechanics and Applications","field":"Physics and Astronomy","cited_by":176,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Army Research Office; Industry Canada; National Security Agency; Ontario Ministry of Research and Innovation; Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Spins; Qubit; Kochen–Specker theorem; Physics; Quantum; Statistical physics; Quantum information processing; Quantum mechanics; Value (mathematics); Computer science; Machine learning; Condensed matter physics","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":[0.006672909,0.0005565255,0.0008811724,0.000525683,0.001553108,0.001836272,0.001641021,0.001085725,0.00142387],"category_scores_gemma":[0.03210458,0.0004870154,0.0003722012,0.0005661732,0.004518868,0.003259031,0.004339326,0.001562796,0.00019231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007038946,"about_ca_system_score_gemma":0.00197453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001071247,"about_ca_topic_score_gemma":0.001648483,"domain_scores_codex":[0.9934464,0.002682939,0.0002198699,0.001129422,0.001832913,0.0006884089],"domain_scores_gemma":[0.9710451,0.01758956,0.003433562,0.00536961,0.001205854,0.001356283],"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":[0.004242718,0.001151493,0.03085127,0.0002374661,0.0003282701,0.001094657,0.001337078,0.1734657,0.1187868,0.6132093,0.000722632,0.0545726],"study_design_scores_gemma":[0.0002582853,0.001231955,0.005740635,0.00004813577,0.0001002405,0.0001996495,0.0002519482,0.5708594,0.1348147,0.2851923,0.00113845,0.0001641812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8433481,0.00006471873,0.154161,0.0003028431,0.00002798893,0.00006237886,0.0001073648,0.0001656177,0.001760096],"genre_scores_gemma":[0.9767693,0.00003172448,0.0227698,0.00004239758,0.00001957286,0.00007472176,0.00004271039,0.00002002544,0.0002298791],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006672909,"threshold_uncertainty_score":0.03529012,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0349154101501817,"score_gpt":0.2984529122327474,"score_spread":0.2635375020825657,"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."}}