{"id":"W2050583339","doi":"10.1145/1543137.1543169","title":"Modeling learning effects in mobile texting","year":2008,"lang":"en","type":"article","venue":"","topic":"Usability and User Interface Design","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Session (web analytics); Human–computer interaction; Plug-in; Key (lock); Subject-matter expert; Domain (mathematical analysis); Mobile phone; Multimedia; Recall; Keypad; Process (computing); Expert system; Artificial intelligence; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002806304,0.00007464077,0.000104103,0.00006914046,0.00009859401,0.0000366691,0.0003531116,0.00003810762,0.00001188018],"category_scores_gemma":[0.00008244152,0.00006856308,0.00003133629,0.000230418,0.00001373077,0.0003742055,0.0001265563,0.0001937782,0.00009782572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002976557,"about_ca_system_score_gemma":0.00002436052,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009197004,"about_ca_topic_score_gemma":0.00001322896,"domain_scores_codex":[0.9991782,0.00008625487,0.0001474264,0.0002542175,0.0001211932,0.0002127269],"domain_scores_gemma":[0.9995716,0.0001407455,0.00001413698,0.0002096885,0.00002480281,0.00003902386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003787035,0.00008661385,0.009008084,0.00002612167,0.000003937493,0.00004882257,0.006979019,0.9592098,0.002278013,0.002748019,0.00004913186,0.01955863],"study_design_scores_gemma":[0.0001361367,0.00006838316,0.0002443343,0.00001719886,3.673502e-7,0.00001303707,0.00005113373,0.9960702,0.002529353,0.0006285278,0.000149424,0.00009190942],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.49165,0.00004901358,0.5063075,0.00002491211,0.00004681473,0.00006951934,7.734858e-9,0.0001128854,0.001739323],"genre_scores_gemma":[0.9623831,0.000006801282,0.03709443,0.00008479854,0.00001302751,0.00001892148,1.141809e-7,0.000004174206,0.000394624],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4707331,"threshold_uncertainty_score":0.2795922,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02865981230822947,"score_gpt":0.2513921476648364,"score_spread":0.2227323353566069,"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."}}