{"id":"W4385492139","doi":"10.1103/physrevlett.131.053803","title":"Super Interferometric Range Resolution","year":2023,"lang":"en","type":"article","venue":"Physical Review Letters","topic":"Advanced Optical Sensing Technologies","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Perimeter Institute; University of Waterloo","funders":"Australian Research Council; Ontario Ministry of Economic Development and Innovation; Ministero dello Sviluppo Economico; Institut Périmètre de physique théorique; Innovation, Science and Economic Development Canada; Government of Canada; Natural Sciences and Engineering Research Council of Canada; Chapman University","keywords":"Physics; Resolution (logic); Interferometry; Optics; Range (aeronautics); Amplitude; Bandwidth (computing); Interference (communication); Limit (mathematics); Measure (data warehouse); Rayleigh scattering; Figure of merit; Dynamic range; Computer science; Mathematical analysis; Telecommunications; Mathematics; Artificial intelligence","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":[0.00008569179,0.0001329115,0.0002659278,0.00008370911,0.00005317751,0.0000149488,0.0001766231,0.000006259801,0.00005071669],"category_scores_gemma":[0.00009282939,0.0001080039,0.0001541251,0.001218601,0.00008544427,0.0001051244,0.0001228778,0.0001922471,0.001373051],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002650832,"about_ca_system_score_gemma":0.000003637659,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008003741,"about_ca_topic_score_gemma":7.736484e-8,"domain_scores_codex":[0.9991259,0.00003311442,0.0001487978,0.0002326324,0.0001604671,0.0002990894],"domain_scores_gemma":[0.9994146,0.0001844918,0.00004150497,0.000291406,0.0000213414,0.00004664516],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003711521,0.0006168744,0.0152956,0.001390872,0.0003548728,0.00005106374,0.000262641,0.001141671,0.07939394,0.1396665,0.3052505,0.4565384],"study_design_scores_gemma":[0.00425899,0.0007694098,0.08341354,0.01370247,0.00127103,0.00001071242,0.0004054965,0.05597053,0.02049598,0.2098255,0.6034806,0.006395766],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9597537,0.001397162,0.01150751,0.0238822,0.0001804353,0.0004705827,0.00001384587,0.0008807116,0.001913838],"genre_scores_gemma":[0.9967349,0.0003318265,0.0006916604,0.001923439,0.0002056309,0.00003582957,0.0000219559,0.00001938351,0.00003540458],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4501427,"threshold_uncertainty_score":0.9994045,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02638282681311806,"score_gpt":0.3087249733843237,"score_spread":0.2823421465712057,"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."}}