{"id":"W2969819528","doi":"10.1088/1367-2630/ab3d97","title":"Realistic sub-Rayleigh imaging with phase-sensitive measurements","year":2019,"lang":"en","type":"article","venue":"New Journal of Physics","topic":"Adaptive optics and wavefront sensing","field":"Physics and Astronomy","cited_by":32,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Advanced Research; National Research Council Canada; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institute for Advanced Research","keywords":"Monte Carlo method; Coherence (philosophical gambling strategy); splice; Intensity (physics); Boosting (machine learning); Phase (matter); Rayleigh scattering; Enhanced Data Rates for GSM Evolution","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":[],"consensus_categories":[],"category_scores_codex":[0.0001211672,0.0001615011,0.0002834141,0.00003576464,0.00005828628,0.00005144434,0.00009019569,0.00001249956,0.00003252037],"category_scores_gemma":[0.00000303527,0.0001273002,0.0001128841,0.0001007037,0.00003117796,0.0002519224,0.00001987115,0.000217136,0.00002799551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000444503,"about_ca_system_score_gemma":0.0001629049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000406231,"about_ca_topic_score_gemma":0.000001007139,"domain_scores_codex":[0.9990195,0.00003037527,0.000247552,0.0001285373,0.000360322,0.0002137277],"domain_scores_gemma":[0.9988841,0.00004120086,0.0004250568,0.0001400493,0.0003812007,0.0001284032],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00139884,0.001632305,0.1514178,0.00004796929,0.002263697,0.0001595819,0.002461124,0.009348098,0.3494037,0.07590947,0.008746158,0.3972112],"study_design_scores_gemma":[0.04281226,0.003538965,0.01125215,0.002479561,0.001946633,0.000198085,0.005352427,0.01936067,0.8218923,0.07789464,0.01027588,0.002996393],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7932102,0.00004239197,0.1906557,0.0001734312,0.0003507703,0.0001585361,0.00001413833,0.000008279871,0.01538657],"genre_scores_gemma":[0.9957432,0.000001067518,0.002955265,0.00005172948,0.001034567,1.231349e-7,0.000006740594,0.00002734682,0.0001799464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4724886,"threshold_uncertainty_score":0.5191153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02433579463426905,"score_gpt":0.2595970211162169,"score_spread":0.2352612264819478,"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."}}