{"id":"W2064228559","doi":"10.1109/t4e.2012.22","title":"The 5R Adaptive Learning Content Generation Platform for Mobile Learning","year":2012,"lang":"en","type":"article","venue":"","topic":"Mobile Learning in Education","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Adaptation (eye); Multimedia; Mobile computing; Context (archaeology); Adaptive learning; Mobile device; Context awareness; Mobile Web; Mobile telephony; Wireless; Mobile technology; Human–computer interaction; World Wide Web; Mobile radio; Computer network; Telecommunications; Geography","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":[0.001707933,0.0007286755,0.0005561392,0.001084746,0.0006662018,0.001804726,0.002215901,0.001655703,0.0111395],"category_scores_gemma":[0.003553144,0.0003837283,0.0007378946,0.0004147978,0.0005896672,0.002034341,0.002361881,0.001634136,0.006471103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000657041,"about_ca_system_score_gemma":0.00119182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001312289,"about_ca_topic_score_gemma":0.000991725,"domain_scores_codex":[0.9989831,0.0002124643,0.00008715336,0.0001593322,0.0004242457,0.000133752],"domain_scores_gemma":[0.9990553,0.0001752091,0.00006772309,0.0002300781,0.0002815973,0.0001901255],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001097981,0.0008697856,0.002104129,0.0007495346,0.00009265589,0.002273094,0.002022162,0.01137138,0.1201597,0.09676708,0.05882857,0.7036639],"study_design_scores_gemma":[0.0006014297,0.001382931,0.003883713,0.0003801828,0.0001567197,0.002789094,0.0002669811,0.2636799,0.08974577,0.02566282,0.6109787,0.0004716437],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.0186475,0.0002690413,0.8829299,0.0006857066,0.0003066516,0.002186725,0.0005465129,0.06780268,0.02662531],"genre_scores_gemma":[0.177728,0.0004048484,0.7618308,0.0009173246,0.0002324389,0.002462997,0.002851163,0.003499617,0.05007284],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.0111395,"threshold_uncertainty_score":0.03726536,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07849516101336713,"score_gpt":0.2870689302547018,"score_spread":0.2085737692413347,"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."}}