{"id":"W2969174528","doi":"10.1007/978-3-319-50832-0","title":"Advances in Visual Computing","year":2016,"lang":"en","type":"book","venue":"Lecture notes in computer science","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":41,"is_retracted":false,"has_abstract":false,"ca_institutions":"Google (Canada)","funders":"","keywords":"Las vegas; Computer science; Computer graphics (images); Set (abstract data type); Volume (thermodynamics); Artificial intelligence; Programming language","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001034977,0.0004268885,0.0005168518,0.001224593,0.0001579514,0.0005712046,0.003686198,0.0002204328,0.00001348526],"category_scores_gemma":[0.0001807333,0.0003495058,0.00008251076,0.001809151,0.0004944427,0.001497679,0.001726622,0.0005034753,0.00007804731],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004794729,"about_ca_system_score_gemma":0.001120547,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004836813,"about_ca_topic_score_gemma":0.00007629334,"domain_scores_codex":[0.9961132,0.00008128097,0.0006289571,0.001450771,0.0009552533,0.0007704906],"domain_scores_gemma":[0.9978909,0.0005730511,0.0003021652,0.0009107419,0.0001684665,0.0001546736],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001641524,0.00004451768,0.0002784581,0.00004342676,0.000001965472,0.00006365829,0.0003181348,0.009788132,0.00001791772,0.009804968,0.0001077234,0.9795294],"study_design_scores_gemma":[0.0003789301,0.0000942096,0.0001215375,0.0009283399,0.00000223749,0.00002379725,1.874271e-7,0.9270673,0.0002566834,0.05736829,0.01312362,0.0006348235],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001556363,0.0006293536,0.9927233,0.0003746992,0.001501446,0.0001947696,0.000004752342,0.0001482287,0.004407924],"genre_scores_gemma":[0.1765649,0.001618962,0.8025575,0.01145216,0.00361928,0.00001733293,0.00008113801,0.0001663349,0.003922373],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9788947,"threshold_uncertainty_score":0.9998957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01270973997319585,"score_gpt":0.30869911657802,"score_spread":0.2959893766048242,"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."}}