{"id":"W3010563898","doi":"10.1038/s41377-020-0261-8","title":"Biologically inspired ultrathin arrayed camera for high-contrast and high-resolution imaging","year":2020,"lang":"en","type":"article","venue":"Light Science & Applications","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"National Research Foundation of Korea; National Research Foundation","keywords":"High resolution; High contrast; Contrast (vision); Computer science; Biological imaging; Superresolution; Optics; Remote sensing; Computer vision; Physics; Geology; Fluorescence","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001515855,0.0001341388,0.0001278018,0.00006680637,0.0005085961,0.0001615934,0.000376668,0.00004364096,0.000005427818],"category_scores_gemma":[0.00003680888,0.0001238034,0.00002498774,0.00069427,0.0002855579,0.0002358041,0.00003370899,0.0001052288,0.00001127524],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004086659,"about_ca_system_score_gemma":0.00005031889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001518195,"about_ca_topic_score_gemma":0.000001776886,"domain_scores_codex":[0.9989993,0.000004795142,0.000212396,0.0003868181,0.0001168948,0.0002798058],"domain_scores_gemma":[0.9994332,0.00003722239,0.00005094235,0.0002209137,0.0001118233,0.0001459207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0000040996,0.00003051554,0.0001073421,0.00003800526,0.000003343477,1.627975e-7,0.0001436017,0.0008360475,0.917789,0.05777391,0.0008862602,0.02238766],"study_design_scores_gemma":[0.0007104171,0.0000837634,0.004210614,0.00003623446,0.000055559,0.000006812701,0.0001176897,0.4568195,0.4321667,0.03577117,0.06921193,0.000809678],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.00958927,0.0001711065,0.9782826,0.009755027,0.00002412202,0.0007460959,0.00005026645,0.0008828479,0.0004986133],"genre_scores_gemma":[0.8724988,0.00002066762,0.1254336,0.0006626054,0.00009429299,0.00124373,0.00002598432,0.00001469747,0.000005596258],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8629096,"threshold_uncertainty_score":0.5048558,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01062361550109955,"score_gpt":0.235427385964989,"score_spread":0.2248037704638894,"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."}}