{"id":"W4386249144","doi":"10.1167/jov.23.9.5494","title":"Making memorability of scenes better or worse by manipulating their contour properties","year":2023,"lang":"en","type":"article","venue":"Journal of Vision","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Artificial intelligence; Perception; Line (geometry); Curvature; Computer science; Line drawings; Computer vision; Orientation (vector space); Contour line; Pattern recognition (psychology); Line segment; Psychology; Mathematics; Geometry; Cartography; Geography; Engineering drawing","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":[0.0005166435,0.0004249063,0.0003967325,0.0002782792,0.0001078035,0.000902598,0.0004134014,0.0005029483,0.002203095],"category_scores_gemma":[0.005261012,0.0003310463,0.0004660689,0.0001649821,0.0004612773,0.001321735,0.0003894739,0.0008757684,0.0001901156],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002601234,"about_ca_system_score_gemma":0.0001439839,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009326595,"about_ca_topic_score_gemma":0.001185337,"domain_scores_codex":[0.9997017,0.00004883753,0.00002478799,0.0001378545,0.0000462564,0.00004062392],"domain_scores_gemma":[0.9969133,0.001248862,0.0009344287,0.0005727051,0.0001415757,0.0001891356],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.002443997,0.0005632775,0.08831257,0.000330209,0.0005239208,0.0002327833,0.000457835,0.009920603,0.8176662,0.001198965,0.0006023629,0.07774724],"study_design_scores_gemma":[0.0002410253,0.003504123,0.592547,0.00006172452,0.0008509738,0.0006543318,0.0003461212,0.08866326,0.3051794,0.005212357,0.002610115,0.0001295335],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9947424,0.0001057714,0.004348398,0.00007778107,0.00001375526,0.00001979054,0.00008150765,0.0001105075,0.0005000131],"genre_scores_gemma":[0.9947765,0.00007766567,0.00451189,0.00004537137,0.000006331036,0.00001581835,0.0001624833,0.00003788868,0.0003661395],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002203095,"threshold_uncertainty_score":0.007370114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1069508234817842,"score_gpt":0.3494635393883662,"score_spread":0.2425127159065819,"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."}}