{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002281124,0.0008718964,0.001022356,0.001098839,0.0003766779,0.001448326,0.001442655,0.002548699,0.00485433],"category_scores_gemma":[0.022702,0.0007710643,0.001020726,0.0005975321,0.001327425,0.002929686,0.001079555,0.001243228,0.0009607592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001376668,"about_ca_system_score_gemma":0.000571563,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007124821,"about_ca_topic_score_gemma":0.0033782,"domain_scores_codex":[0.9990551,0.0003517673,0.00005422368,0.0002234068,0.0001400514,0.000175452],"domain_scores_gemma":[0.9776402,0.01831357,0.001845333,0.0009855798,0.0007447063,0.0004706907],"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":[0.0004537699,0.0002857135,0.008389534,0.0001668596,0.00006608434,0.0003255028,0.0005558894,0.9516999,0.004273355,0.01433028,0.000287981,0.01916528],"study_design_scores_gemma":[0.00004448156,0.0001727147,0.001882082,0.00001316673,0.00002692599,0.00007082568,0.00004200431,0.9894667,0.001186903,0.006822261,0.0002513413,0.00002057859],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8351444,0.0004812096,0.1564535,0.000462096,0.00003243183,0.0003102097,0.0003434723,0.0005740144,0.006198647],"genre_scores_gemma":[0.9886984,0.000178722,0.006668687,0.00003901004,0.00001598419,0.0001135477,0.00007914611,0.00004652382,0.004159922],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007124821,"threshold_uncertainty_score":0.01623935,"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."}}