{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006344165,0.0004500755,0.0004640739,0.000532878,0.0003269531,0.0007900232,0.0009336558,0.0005939733,0.002270998],"category_scores_gemma":[0.001895384,0.0002787059,0.0002557047,0.0005980404,0.001011503,0.002250218,0.00187982,0.001081051,0.0006980717],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000398477,"about_ca_system_score_gemma":0.0002953993,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001875981,"about_ca_topic_score_gemma":0.0002631437,"domain_scores_codex":[0.9992544,0.0001050521,0.00001856763,0.0001445492,0.0003926006,0.00008476694],"domain_scores_gemma":[0.9988874,0.0004844754,0.000180832,0.0002616751,0.0001404613,0.00004513867],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002588551,0.00009044532,0.002076474,0.0002712832,0.00006142019,0.000262596,0.0003147422,0.02848138,0.6093735,0.2381682,0.002561034,0.1180801],"study_design_scores_gemma":[0.00004261955,0.0004054261,0.00336174,0.0000564021,0.00005280856,0.001748958,0.000178898,0.4301155,0.3902579,0.1498038,0.02388777,0.00008829974],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1030341,0.002133552,0.8616717,0.0006317042,0.0001175393,0.00003800117,0.0001763616,0.0009815316,0.03121543],"genre_scores_gemma":[0.759509,0.0009733615,0.2359391,0.0003998483,0.0001762781,0.00006653018,0.0001946055,0.00008552174,0.002655854],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002270998,"threshold_uncertainty_score":0.007597268,"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."}}