{"id":"W3134596229","doi":"10.1145/3406522.3446041","title":"Visualizing Searcher Gaze Patterns","year":2021,"lang":"en","type":"article","venue":"","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; CLARITY; Eye tracking; Gaze; Visualization; Information retrieval; Sample (material); Tracking (education); Information visualization; Tag cloud; Data visualization; Human–computer interaction; Artificial intelligence","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":[0.001052946,0.0007392665,0.0004373488,0.007198748,0.0003739223,0.001320435,0.0003374686,0.0004665884,0.006791563],"category_scores_gemma":[0.006623856,0.0001836626,0.0004090097,0.003715256,0.0001672285,0.001445395,0.0009265429,0.000472907,0.001218649],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003576148,"about_ca_system_score_gemma":0.0003883261,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005111554,"about_ca_topic_score_gemma":0.005507848,"domain_scores_codex":[0.9995072,0.0001419953,0.00004219399,0.0001287411,0.0001309027,0.00004890003],"domain_scores_gemma":[0.9959186,0.002466825,0.0004607369,0.0002999224,0.0007247644,0.0001290964],"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":[0.001786155,0.0002613765,0.1189154,0.002585194,0.0005493166,0.0006794277,0.02674901,0.01585723,0.09583364,0.009530183,0.06618857,0.6610646],"study_design_scores_gemma":[0.0002650308,0.0009079103,0.4826217,0.001049491,0.0005113076,0.001627509,0.01567573,0.2618075,0.07558362,0.03108742,0.1283153,0.0005474013],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7049203,0.0032716,0.2101674,0.001436191,0.0002144038,0.0003480737,0.03399563,0.02106979,0.02457663],"genre_scores_gemma":[0.8939109,0.0009518997,0.09236114,0.0001245174,0.0000849433,0.0004126992,0.006253499,0.001025816,0.004874623],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007198748,"threshold_uncertainty_score":0.02272004,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05185587392364011,"score_gpt":0.3498405323967137,"score_spread":0.2979846584730736,"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."}}