{"id":"W2739232917","doi":"10.3934/bdia.2017003","title":"Rendering website traffic data into interactive taste graph visualizations","year":2017,"lang":"en","type":"article","venue":"Big Data and Information Analytics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario College of Art and Design","funders":"","keywords":"Rendering (computer graphics); Computer science; Computer graphics (images); Graph; World Wide Web; Multimedia; Theoretical computer science","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":[0.001218787,0.001010054,0.0005024433,0.003633659,0.0005358773,0.002953465,0.0007415218,0.0006823019,0.01706508],"category_scores_gemma":[0.007921466,0.0004611573,0.0008179308,0.002130938,0.0004210137,0.00190464,0.002280368,0.001312721,0.002581463],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003470176,"about_ca_system_score_gemma":0.000545756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002078062,"about_ca_topic_score_gemma":0.003661222,"domain_scores_codex":[0.999473,0.0001478007,0.00004434284,0.00009307736,0.0001916337,0.00005023576],"domain_scores_gemma":[0.9954249,0.002662569,0.0001889628,0.0007582429,0.0007785839,0.0001868555],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001821467,0.0008146961,0.0164194,0.002469458,0.0004187482,0.002028816,0.01615878,0.0443648,0.1016958,0.06078639,0.2063671,0.5466546],"study_design_scores_gemma":[0.0004254012,0.0003815047,0.02053277,0.0004903211,0.0002251872,0.001115117,0.005270188,0.3826819,0.06629714,0.1351105,0.3870005,0.0004694275],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07094362,0.0002923533,0.8071769,0.001304014,0.0005581771,0.0005214153,0.02311168,0.07565498,0.02043681],"genre_scores_gemma":[0.3210499,0.0004694086,0.6426041,0.0005049731,0.000210236,0.001113542,0.01738315,0.009175162,0.007489609],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01706508,"threshold_uncertainty_score":0.05708838,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.110875864836745,"score_gpt":0.3583582706958002,"score_spread":0.2474824058590552,"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."}}