{"id":"W2961821656","doi":"10.1103/physreva.100.042325","title":"Exploration of an augmented set of Leggett-Garg inequalities using a noninvasive continuous-in-time velocity measurement","year":2019,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Quantum Mechanics and Applications","field":"Physics and Astronomy","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Set (abstract data type); Ideal (ethics); Protocol (science); Sign (mathematics); Continuous variable; Computer science; Mathematics; Algorithm; Physics; Statistical physics; Mathematical optimization; Mathematical analysis","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0007871825,0.0003655362,0.001709291,0.00005035005,0.00004784344,0.00001822274,0.000411581,0.00002572781,0.0001011485],"category_scores_gemma":[0.00008362085,0.0003130659,0.0005220855,0.0005709634,0.00006963706,0.0004227536,0.0001268598,0.0002491071,0.0001211641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007564088,"about_ca_system_score_gemma":0.0001566736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001511478,"about_ca_topic_score_gemma":0.000002662686,"domain_scores_codex":[0.9969746,0.0003527588,0.0009647295,0.0005055018,0.000851695,0.0003506818],"domain_scores_gemma":[0.9976974,0.0001515357,0.0008037886,0.0007228621,0.0004820395,0.0001424304],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00005913605,0.004889289,0.0009727125,0.01313798,0.0003444999,0.000001694527,0.000847513,0.0000640689,0.5461578,0.4081927,0.000346764,0.02498579],"study_design_scores_gemma":[0.003171871,0.001546496,0.0008045814,0.0671848,0.001928603,0.000002041551,0.0005273455,0.06474859,0.366981,0.4808028,0.009980489,0.002321449],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9877005,0.003935625,0.00507451,0.0002630394,0.00003971531,0.002310231,0.0001574768,0.00002181081,0.0004971497],"genre_scores_gemma":[0.9970143,0.002015221,0.0001773993,0.0001643468,0.000193165,0.0002512118,0.0001343821,0.00003656436,0.00001336971],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1791769,"threshold_uncertainty_score":0.9999322,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06055127204103074,"score_gpt":0.3653857381458026,"score_spread":0.3048344661047719,"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."}}