{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001104558,0.00004747223,0.00005596455,0.00003218577,0.00004294729,0.0002500366,0.0002967443,0.0000185976,0.0006154076],"category_scores_gemma":[0.00003560114,0.00004301337,0.00002784044,0.0002976402,0.000006902442,0.0002739991,0.0002784715,0.00004170273,0.0002419412],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001004411,"about_ca_system_score_gemma":0.00005473449,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009287448,"about_ca_topic_score_gemma":0.00001353085,"domain_scores_codex":[0.999361,0.00004234647,0.00009328112,0.0001895925,0.0001781755,0.0001356122],"domain_scores_gemma":[0.9994851,0.00002391543,0.0000148532,0.0003239153,0.00008411001,0.00006812473],"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":[2.394743e-7,0.00009502528,0.006836174,0.00001824549,0.00001665791,0.00008715253,0.0003427038,0.00001608357,0.001191443,0.9548968,0.01382728,0.02267216],"study_design_scores_gemma":[0.0005774759,0.0000380529,0.007423101,0.00006023405,0.000008275883,0.00005598773,0.0005300584,0.6153712,0.05702941,0.00374403,0.3146321,0.0005300338],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001494328,0.0000227589,0.9813276,0.0009969648,0.0000924362,0.00001384504,0.000002024287,0.0001216668,0.0159284],"genre_scores_gemma":[0.8793542,0.0001266332,0.06067274,0.0127631,0.0001482864,0.000003384709,0.00007169165,0.0000187135,0.0468412],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9511528,"threshold_uncertainty_score":0.6738282,"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."}}