{"id":"W2103614292","doi":"10.1145/1753326.1753337","title":"Effects of interior bezels of tiled-monitor large displays on visual search, tunnel steering, and target selection","year":2010,"lang":"en","type":"article","venue":"","topic":"Interactive and Immersive Displays","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"University of Toronto","keywords":"Computer science; Selection (genetic algorithm); Affect (linguistics); Visual search; Human–computer interaction; Simulation; Computer vision; 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":[0.0004653044,0.0009212524,0.0005907566,0.0003071735,0.0002802208,0.0008264824,0.0004482026,0.0004878848,0.004283316],"category_scores_gemma":[0.01113465,0.000436805,0.0003763326,0.0002346933,0.0004714014,0.0007995985,0.001180809,0.0005576573,0.0003060496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001661379,"about_ca_system_score_gemma":0.0001674655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006894624,"about_ca_topic_score_gemma":0.0007979715,"domain_scores_codex":[0.999527,0.0001609895,0.00005660115,0.00008054634,0.0001126914,0.0000621389],"domain_scores_gemma":[0.9916601,0.006215191,0.000603564,0.000692,0.0004015257,0.0004275434],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0176945,0.001192705,0.01157591,0.001221352,0.0001314634,0.0005509988,0.001799449,0.01089237,0.8542943,0.0006662723,0.0005322779,0.09944841],"study_design_scores_gemma":[0.002388332,0.03106871,0.2729881,0.0007227869,0.001192257,0.002530236,0.002235649,0.1050334,0.5682195,0.002781585,0.01043646,0.000402993],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9840105,0.000260668,0.01378968,0.00005571091,0.00003772321,0.0001087833,0.0001244237,0.00037355,0.001238872],"genre_scores_gemma":[0.9762249,0.0002154856,0.02241985,0.00007316977,0.000013679,0.0001056804,0.0001145303,0.0001098691,0.0007226558],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004283316,"threshold_uncertainty_score":0.01432908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003563434917097499,"score_gpt":0.2572657693008826,"score_spread":0.2537023343837851,"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."}}