{"id":"W2029923333","doi":"10.1145/2470654.2481295","title":"SeeSay and HearSay CAPTCHA for mobile interaction","year":2013,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"CAPTCHA; Hearsay; Computer science; Human–computer interaction; Modality (human–computer interaction); Text entry; Mobile device; Usability; World Wide Web","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":[0.0005061397,0.0007388407,0.0002647076,0.0003820262,0.0003996483,0.0009165351,0.0006289086,0.00129206,0.00960219],"category_scores_gemma":[0.003068697,0.0002153466,0.0003068021,0.0003026449,0.0008438536,0.001197423,0.0007706379,0.0007933934,0.001829444],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002211149,"about_ca_system_score_gemma":0.0002522817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003522554,"about_ca_topic_score_gemma":0.0006756941,"domain_scores_codex":[0.9992697,0.0002660706,0.00004884241,0.00009347336,0.0002782324,0.00004368683],"domain_scores_gemma":[0.9983602,0.000945364,0.0001538175,0.0002185176,0.0002478221,0.00007435182],"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":[0.001140573,0.00009951537,0.0004793766,0.001558847,0.00004445902,0.0007234641,0.001350134,0.0033077,0.4403339,0.02446943,0.006659847,0.5198327],"study_design_scores_gemma":[0.0003219315,0.005872702,0.005704505,0.0008377403,0.0002245645,0.01051435,0.001239743,0.1238345,0.5522953,0.02198332,0.2767488,0.00042254],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06614088,0.004790146,0.8930484,0.0007836548,0.0005640563,0.0005299114,0.0001357747,0.003812378,0.03019483],"genre_scores_gemma":[0.5467332,0.002393318,0.4271666,0.00074185,0.0003511884,0.0006227805,0.0001889264,0.0003461363,0.02145606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00960219,"threshold_uncertainty_score":0.03212249,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287094194968495,"score_gpt":0.2541684388752869,"score_spread":0.241297496925602,"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."}}