{"id":"W4402731606","doi":"10.1103/physreva.110.033716","title":"Optimizing measurement tradeoffs in multiparameter spatial superresolution","year":2024,"lang":"en","type":"article","venue":"Physical review. A/Physical review, A","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada; University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; Kavli Institute for Theoretical Physics, University of California, Santa Barbara; Agencia Estatal de Investigación; National Science Foundation","keywords":"Superresolution; Computer science; Artificial intelligence; Image (mathematics)","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":[0.009001736,0.001252392,0.001159589,0.001060225,0.000717082,0.002142535,0.001613765,0.002345512,0.001068277],"category_scores_gemma":[0.04181014,0.0009358739,0.0003600593,0.001483183,0.002518768,0.005974111,0.00383323,0.001731271,0.0003596116],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00158411,"about_ca_system_score_gemma":0.0007152996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005243354,"about_ca_topic_score_gemma":0.0006313223,"domain_scores_codex":[0.9949629,0.002837029,0.0002068626,0.0006736715,0.001086013,0.000233514],"domain_scores_gemma":[0.9847147,0.01164703,0.001010111,0.001622043,0.0008055057,0.0002006799],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007671509,0.0002040666,0.002055947,0.0006899115,0.000209214,0.0003267617,0.0004862316,0.3795521,0.1232951,0.3144569,0.001657532,0.176299],"study_design_scores_gemma":[0.00005800816,0.0001834546,0.001093732,0.00009206,0.0000405252,0.0002361231,0.0001427727,0.8064967,0.03474953,0.1545973,0.002211139,0.00009869676],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04826427,0.001801647,0.9448484,0.0009467044,0.0000337543,0.00006226137,0.00006338802,0.0002547757,0.003724755],"genre_scores_gemma":[0.6753038,0.000985623,0.3222961,0.0001891304,0.00006550132,0.000182055,0.00007929236,0.0001283397,0.0007701846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009001736,"threshold_uncertainty_score":0.04760629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06647568061870741,"score_gpt":0.3762558403558299,"score_spread":0.3097801597371225,"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."}}