{"id":"W2103129280","doi":"10.1002/j.2168-0159.2014.tb00133.x","title":"36.1: Wide Field of View Compressive Light Field Display using a Multilayer Architecture and Tracked Viewers","year":2014,"lang":"en","type":"article","venue":"SID Symposium Digest of Technical Papers","topic":"Advanced Optical Imaging Technologies","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; University of North Carolina at Chapel Hill; Samsung; National Research Foundation Singapore","keywords":"Computer science; Light field; Field (mathematics); Computer graphics (images); Field of view; Software; Computer vision; Depth of field; Artificial intelligence; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001269969,0.0003062057,0.0001974132,0.0002303995,0.0002214628,0.0005809402,0.0003774058,0.0002823079,0.003422299],"category_scores_gemma":[0.0002594804,0.0001650522,0.0002230562,0.00015816,0.0001711067,0.0004705247,0.0003986569,0.0003230162,0.0004539038],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003753718,"about_ca_system_score_gemma":0.0002663268,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002405324,"about_ca_topic_score_gemma":0.003074053,"domain_scores_codex":[0.9999177,0.000008124416,0.000004229904,0.00001247071,0.00003784755,0.0000196191],"domain_scores_gemma":[0.999868,0.00002523086,0.00001876105,0.00002229692,0.00003649245,0.00002924682],"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":[0.000295982,0.0001294334,0.00176362,0.00008898295,0.00003099211,0.0004827261,0.0002066309,0.006734315,0.9387958,0.004007886,0.004928124,0.04253547],"study_design_scores_gemma":[0.0001476205,0.001206456,0.01153766,0.00006691366,0.00009397597,0.00148535,0.0001718953,0.1704155,0.77338,0.001342586,0.03993794,0.0002141127],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8468891,0.0004027224,0.125534,0.0007106207,0.0001644288,0.0001027083,0.0005482931,0.003803173,0.02184496],"genre_scores_gemma":[0.9022899,0.0002329832,0.08598744,0.0001506139,0.00003341718,0.00004177136,0.0002565543,0.0001318589,0.01087538],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003422299,"threshold_uncertainty_score":0.01144868,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005740888076077344,"score_gpt":0.2410166335169992,"score_spread":0.2352757454409218,"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."}}