{"id":"W2952295476","doi":"10.25039/x46.2019.pp04","title":"DETECTION OF THE STROBOSCOPIC EFFECT UNDER LOW LEVELS OF THE STROBOSCOPIC VISIBILITY MEASURE","year":2019,"lang":"en","type":"article","venue":"PROCEEDINGS OF the 29th Quadrennial Session of the CIE","topic":"Visual perception and processing mechanisms","field":"Neuroscience","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"Centre Scientifique et Technique du Bâtiment; Pacific Northwest National Laboratory; Danmarks Tekniske Universitet","keywords":"Stroboscope; Visibility; Support vector machine; Population; Computer science; Artificial intelligence; Measure (data warehouse); SIGNAL (programming language); Range (aeronautics); Computer vision; Optics; Physics; Engineering; Data mining","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":[0.001111937,0.0002715354,0.0004624697,0.00005871196,0.00037915,0.0000369738,0.001948811,0.0001992203,0.0001379224],"category_scores_gemma":[0.001254598,0.0001183221,0.0004438157,0.0009108481,0.000512442,0.000227054,0.0006979994,0.000497641,0.000005710935],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009003962,"about_ca_system_score_gemma":0.0001477636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005281352,"about_ca_topic_score_gemma":0.000009468269,"domain_scores_codex":[0.9971249,0.0002803808,0.0006419308,0.0004366818,0.001223088,0.0002930003],"domain_scores_gemma":[0.9974673,0.0002093885,0.001344891,0.0006206367,0.0003049337,0.00005288951],"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":[0.0001304274,0.000114812,0.007040857,0.0005683416,0.000009906523,6.405819e-9,0.0003617428,0.0001345718,0.9900516,0.0003603059,0.00003673179,0.001190748],"study_design_scores_gemma":[0.0006750612,0.0002401033,0.08176638,0.001134847,0.00006100109,0.000002748586,0.0001736846,0.0003656714,0.9132973,0.002160815,0.00001383914,0.0001085783],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9961777,0.00002442504,0.00008614516,0.0005243013,0.001563181,0.001038425,0.00003012139,0.00002664296,0.0005291123],"genre_scores_gemma":[0.9989936,0.000003524579,0.00003025628,0.000171937,0.00005637008,0.000009623673,1.193694e-7,0.00002601841,0.0007085324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07675428,"threshold_uncertainty_score":0.4825037,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03213769844524414,"score_gpt":0.2926111542662396,"score_spread":0.2604734558209955,"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."}}