{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001071484,0.0001176851,0.00009458104,0.00004419345,0.0008864305,0.000241501,0.0003722739,0.00005846962,0.00002222286],"category_scores_gemma":[0.0002946652,0.00008235897,0.00006438248,0.0001458568,0.00003281356,0.0008245471,0.0001029449,0.0002707153,0.000096986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001093967,"about_ca_system_score_gemma":0.00005246324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002645511,"about_ca_topic_score_gemma":0.000009061619,"domain_scores_codex":[0.9988222,0.0001027292,0.0002123658,0.0002275422,0.0002138706,0.0004213212],"domain_scores_gemma":[0.9988037,0.0005311039,0.0001573821,0.0002521109,0.0001672974,0.0000884019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002017828,0.0001225433,0.008705028,0.000009416947,0.00005097559,1.833141e-7,0.009157905,0.03598114,0.005108521,0.2689902,0.005193328,0.6666606],"study_design_scores_gemma":[0.0002597003,0.0004042123,0.002343902,0.000007858273,0.000008113253,0.000008311118,0.002241724,0.6575358,0.003690972,0.0002333921,0.3330463,0.0002197464],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06268835,0.0004456871,0.9297579,0.0003281588,0.001381156,0.0006523074,1.454389e-7,0.0002575758,0.004488718],"genre_scores_gemma":[0.958141,0.00002944697,0.02955076,0.0001097836,0.0006242582,0.0006030079,0.000008581518,0.00001468123,0.01091846],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9002072,"threshold_uncertainty_score":0.6817796,"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."}}