{"id":"W2157689946","doi":"10.1109/tvcg.2010.149","title":"eSeeTrack—Visualizing Sequential Fixation Patterns","year":2010,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Data Visualization and Analytics","field":"Computer Science","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Visualization; Eye tracking; Gaze; Timeline; Outlier; Data visualization; Artificial intelligence; Fixation (population genetics); Visual analytics; Computer vision; Data mining; Pattern recognition (psychology)","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":[0.0007151748,0.0008221842,0.0004069541,0.002453603,0.00032307,0.001342658,0.0009306725,0.0007271413,0.01107301],"category_scores_gemma":[0.004175973,0.0003355014,0.0004994015,0.001101679,0.0001993657,0.001950383,0.001326418,0.0007818551,0.00117461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002536652,"about_ca_system_score_gemma":0.0004514311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003372826,"about_ca_topic_score_gemma":0.004153166,"domain_scores_codex":[0.9997587,0.00005767625,0.00002114914,0.00004717681,0.00008375086,0.00003161718],"domain_scores_gemma":[0.9981954,0.0009350105,0.0001779105,0.0002194218,0.0003208623,0.0001515483],"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":[0.002124554,0.0003599684,0.02279113,0.001587268,0.0003173873,0.001212281,0.006846552,0.02443842,0.1280501,0.01128758,0.07665668,0.724328],"study_design_scores_gemma":[0.00051367,0.001014297,0.05323989,0.0006672072,0.0002185266,0.002561487,0.003720563,0.6007804,0.1295197,0.03695626,0.1703259,0.000482196],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1097805,0.0006956484,0.8276535,0.0007325239,0.0001925723,0.000252842,0.009301501,0.04426454,0.007126364],"genre_scores_gemma":[0.4441751,0.0007602901,0.541714,0.0001749378,0.00008840053,0.0004100676,0.005444309,0.002824856,0.004407959],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01107301,"threshold_uncertainty_score":0.03704298,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0235894024058024,"score_gpt":0.3001605937277248,"score_spread":0.2765711913219224,"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."}}