{"id":"W2982396568","doi":"10.1103/physrevlett.123.180602","title":"Collisional Quantum Thermometry","year":2019,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Quantum Information and Cryptography","field":"Computer Science","cited_by":118,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Centre for Quantum Technologies; Ministry of Education - Singapore; National Research Foundation","keywords":"Qubit; Quantum metrology; Physics; Statistical physics; Metrology; Quantum; Computer science; Feature (linguistics); Algorithm; Quantum mechanics; Quantum information; Quantum network","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001637553,0.0001116639,0.0002168983,0.0000606159,0.0000330873,0.00005697741,0.0006246392,0.000005826842,0.00007865072],"category_scores_gemma":[0.00001509301,0.00008270736,0.0002025279,0.000675449,0.00002572729,0.0004882825,0.00009903545,0.0001173094,0.002883624],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001240485,"about_ca_system_score_gemma":0.00001507369,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001594726,"about_ca_topic_score_gemma":2.561176e-8,"domain_scores_codex":[0.999016,0.000048896,0.0001823071,0.0001890235,0.0003759476,0.0001878412],"domain_scores_gemma":[0.9992613,0.00009745382,0.00008361343,0.0004501908,0.00003055831,0.00007687059],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000004061674,0.0002293254,0.0008982192,0.001031252,0.00004709567,0.000005082616,0.0003003529,0.00007222161,0.008609928,0.9071455,0.06328282,0.01837417],"study_design_scores_gemma":[0.00130771,0.0003027859,0.02921287,0.003015421,0.00005314103,0.00003355002,0.00002941393,0.3275599,0.0009438313,0.01485776,0.6210753,0.001608309],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8496377,0.004603212,0.08520894,0.05428055,0.0006658809,0.0008025438,0.00000452457,0.0003394841,0.004457127],"genre_scores_gemma":[0.9015238,0.0007796888,0.000879403,0.09671681,0.00006043345,0.0000199622,0.000004059398,0.000006461065,0.000009434375],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8922877,"threshold_uncertainty_score":0.9978927,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01070894461612689,"score_gpt":0.2667445897223847,"score_spread":0.2560356451062579,"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."}}