{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002875201,0.0002558738,0.0001970445,0.0005454622,0.0004541887,0.0006178094,0.0004374673,0.0001783023,0.00006241276],"category_scores_gemma":[0.000005782807,0.0002636968,0.0001052671,0.0008981448,0.00008108317,0.0008658834,0.00001158714,0.0003393115,0.00002551748],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001474828,"about_ca_system_score_gemma":0.00005210995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002460278,"about_ca_topic_score_gemma":0.00009307774,"domain_scores_codex":[0.9982136,0.0001124658,0.0004107878,0.0005500008,0.0004384306,0.0002747114],"domain_scores_gemma":[0.9988704,0.00008349081,0.0001497047,0.0004854868,0.0002103196,0.0002005674],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005844116,0.0002757608,0.0002995128,0.00003480203,0.00003954599,0.000005066894,0.0006178812,0.0002078457,0.0002813365,0.9838606,0.0002601957,0.01411158],"study_design_scores_gemma":[0.0005302542,0.0001350051,0.0006408311,0.00003238295,0.00002304033,0.00002924961,0.00002264184,0.9889973,0.005137082,0.0007886788,0.003317749,0.0003457615],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02271725,0.000005838782,0.9747338,0.00009109676,0.00175688,0.0001687578,0.00002542005,0.000430422,0.00007053839],"genre_scores_gemma":[0.9953246,0.0001419059,0.002334814,0.001854276,0.0001507118,0.00001414864,0.00004973362,0.00002806661,0.0001018217],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9887895,"threshold_uncertainty_score":0.9999815,"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."}}