{"id":"W3157649142","doi":"10.24908/iqurcp.8418","title":"Initial Use of Foreground Objects in Understanding Visual Scenes","year":2016,"lang":"en","type":"article","venue":"Inquiry Queen s Undergraduate Research Conference Proceedings","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Object (grammar); Computer vision; Set (abstract data type); Artificial intelligence; Computer science; Scene statistics; Scale (ratio); Perception; Geography; Psychology; Cartography","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001716266,0.000234842,0.0003267478,0.001238433,0.0002315396,0.0005711244,0.0008020173,0.0001661917,0.00001483163],"category_scores_gemma":[0.0007308166,0.0001789088,0.0000894794,0.001804861,0.0007031817,0.00348091,0.0005227006,0.000375944,0.00003820324],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005176659,"about_ca_system_score_gemma":0.0003926458,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002837434,"about_ca_topic_score_gemma":0.0000989625,"domain_scores_codex":[0.9962782,0.0001829139,0.0005828007,0.000741046,0.001344981,0.0008700847],"domain_scores_gemma":[0.9978591,0.00046288,0.0001826678,0.0002363788,0.001041117,0.0002178566],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001362855,0.0002404545,0.01323097,0.0001320296,0.00003049402,0.00002162022,0.002915555,0.000001522593,0.04019781,0.9244186,0.0004225158,0.01825217],"study_design_scores_gemma":[0.002735217,0.001904289,0.01064992,0.00176393,0.000009474513,0.00004936745,0.004638808,0.01653737,0.05845726,0.901892,0.000494152,0.000868216],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.48019,0.00001451288,0.5072277,0.01007018,0.0004084438,0.0006113733,0.000002697584,0.0002151773,0.001259936],"genre_scores_gemma":[0.9984525,0.0001878545,0.0009117459,0.00004068514,0.00008279007,0.0000503947,0.000001029391,0.00002016362,0.0002528679],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5182624,"threshold_uncertainty_score":0.729569,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2852403245496888,"score_gpt":0.4192901345693992,"score_spread":0.1340498100197104,"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."}}