{"id":"W2805058177","doi":"10.1145/3205929.3205933","title":"GaRSIVis","year":2018,"lang":"en","type":"article","venue":"","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Institute for Computing, Information and Cognitive Systems","keywords":"Computer science; Gaze; Visualization; Data visualization; Reading (process); Filter (signal processing); Human–computer interaction; Artificial intelligence; Data modeling; Machine learning; Data mining; Computer vision; Database","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001294169,0.001702909,0.0009062255,0.002519407,0.0004241061,0.002518333,0.001750876,0.0008389405,0.03838266],"category_scores_gemma":[0.006559189,0.0007416091,0.00115632,0.001278199,0.0003092159,0.002410459,0.002163657,0.001413158,0.01406381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006798342,"about_ca_system_score_gemma":0.001001997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007142884,"about_ca_topic_score_gemma":0.01032486,"domain_scores_codex":[0.9992529,0.0001166053,0.00004985028,0.0002707111,0.000255062,0.00005503208],"domain_scores_gemma":[0.9974663,0.001129755,0.0001822296,0.0006866971,0.0003680669,0.0001669553],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001770456,0.0003592751,0.02377068,0.002216873,0.0005034854,0.000667312,0.002451849,0.01321415,0.01929273,0.01739478,0.3736931,0.5446653],"study_design_scores_gemma":[0.000449501,0.0006304745,0.03272545,0.0007374478,0.0002866485,0.001002778,0.0007462488,0.2564938,0.03239775,0.04041218,0.6337788,0.0003387878],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"software","genre_gemma":"other","genre_scores_codex":[0.03856694,0.00218432,0.2197085,0.001651558,0.0004961018,0.0007329088,0.1100965,0.5761495,0.05041384],"genre_scores_gemma":[0.3561383,0.003069718,0.3997963,0.0009557561,0.0003562942,0.001449468,0.1542923,0.03421184,0.04973001],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.03838266,"threshold_uncertainty_score":0,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.476069697491297,"score_gpt":0.5084823223662762,"score_spread":0.0324126248749792,"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."}}