{"id":"W4254490032","doi":"10.31219/osf.io/8b9xs","title":"Evaluating 'Graphical Perception' with CNNs","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Visualization; Perception; Convolutional neural network; Task (project management); Artificial intelligence; Visual perception; Human–computer interaction; Pattern recognition (psychology); Psychology; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003066781,0.001398282,0.0004932553,0.001124115,0.0002301527,0.001677045,0.0009550247,0.001157163,0.003331222],"category_scores_gemma":[0.02345985,0.0003321214,0.0005562328,0.0006940021,0.0006552525,0.003242377,0.001412496,0.001061362,0.0006910988],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009570764,"about_ca_system_score_gemma":0.0004648673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004493173,"about_ca_topic_score_gemma":0.005596465,"domain_scores_codex":[0.9986188,0.0004561698,0.0001060001,0.0003504844,0.0003085278,0.0001600387],"domain_scores_gemma":[0.9921499,0.004980044,0.0007558838,0.0009261043,0.0008391447,0.000348823],"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":[0.002705271,0.0005907224,0.05686789,0.001541832,0.0008398059,0.0003304979,0.0006612881,0.3593499,0.04598616,0.007820513,0.01139808,0.511908],"study_design_scores_gemma":[0.00006750673,0.0008117477,0.02177968,0.0001044488,0.000142895,0.0001332266,0.0002206367,0.937993,0.02594888,0.009925966,0.002821313,0.00005066939],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8687475,0.002059809,0.1133029,0.0009718163,0.000258553,0.000164553,0.001437228,0.002626926,0.01043066],"genre_scores_gemma":[0.9689578,0.0004213165,0.02797622,0.0001308294,0.00003821326,0.00004137584,0.001366301,0.0001026078,0.0009652592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004493173,"threshold_uncertainty_score":0.01621884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09518307370229437,"score_gpt":0.4070941792039013,"score_spread":0.3119111055016069,"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."}}