{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007640557,0.0003469267,0.0003602947,0.0004345377,0.0002155204,0.0005485801,0.0003458885,0.000466123,0.003405338],"category_scores_gemma":[0.006400348,0.0002137542,0.0001436184,0.000193781,0.0004724906,0.0004801182,0.000894302,0.0007564004,0.0003210299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002032982,"about_ca_system_score_gemma":0.0002674142,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000543271,"about_ca_topic_score_gemma":0.000598312,"domain_scores_codex":[0.9990782,0.0002072979,0.0001024271,0.0002108881,0.0002796257,0.0001216186],"domain_scores_gemma":[0.9960631,0.002021111,0.0007868501,0.0003537391,0.0004133005,0.0003618675],"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":[0.002681095,0.0005665355,0.007783028,0.0002039934,0.00004433443,0.00007704159,0.0005872051,0.0001662859,0.9721488,0.0007199543,0.0002819817,0.01473978],"study_design_scores_gemma":[0.0003882466,0.01650175,0.5285056,0.0001111933,0.0001585218,0.0008240837,0.0006736189,0.006901575,0.440488,0.003023226,0.002358833,0.0000653893],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.993083,0.000129527,0.003621892,0.00004578445,0.00003178557,0.00006845494,0.0000541517,0.00004646918,0.002918891],"genre_scores_gemma":[0.9953406,0.00009744713,0.003332028,0.00008175143,0.00002489918,0.00008613311,0.00009491936,0.00003102635,0.0009111724],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003405338,"threshold_uncertainty_score":0.011392,"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."}}