{"id":"W2480044500","doi":"10.4018/978-1-59904-840-6.ch002","title":"Context-Aware Mobile Learning on the Semantic Web","year":2008,"lang":"en","type":"book-chapter","venue":"IGI Global eBooks","topic":"Context-Aware Activity Recognition Systems","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University","funders":"","keywords":"Computer science; Context (archaeology); Context awareness; Mobile device; Ubiquitous computing; Mobile technology; World Wide Web; Mobile computing; Adaptability; Discoverability; Human–computer interaction; Multimedia; Knowledge management; Telecommunications","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003069228,0.0006743616,0.0007225315,0.0001223089,0.0004911107,0.000345787,0.001569942,0.0004607129,0.00008302221],"category_scores_gemma":[0.0000513142,0.0005412454,0.0004688663,0.00005631618,0.0001910932,0.0001514335,0.0005373643,0.001001087,0.002999164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003506333,"about_ca_system_score_gemma":0.0004274111,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009827596,"about_ca_topic_score_gemma":0.0001441751,"domain_scores_codex":[0.9968243,0.0001739761,0.0005628597,0.001001735,0.0009135169,0.0005236719],"domain_scores_gemma":[0.997209,0.0005017892,0.0005233574,0.001294294,0.0002780076,0.0001935415],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002135886,0.00002754659,0.00003235545,0.00003917109,0.0002209653,0.0003599254,0.0004571823,0.00001305153,0.00002180341,0.8685209,0.02013495,0.1101508],"study_design_scores_gemma":[0.0008059294,0.0005684512,0.0000307102,0.001264506,0.00006332457,0.0009905393,0.0001163728,0.002427927,0.0001102384,0.02700769,0.9651398,0.001474499],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.0004592146,0.0004320533,0.005445946,0.0002894597,0.001071006,0.001102871,0.00006509654,0.0007102493,0.9904241],"genre_scores_gemma":[0.8739715,0.00001381363,0.00002770759,0.001229957,0.0003007062,0.0001042328,0.000003120184,0.00005189577,0.1242971],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9450049,"threshold_uncertainty_score":0.9997039,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03090607458860964,"score_gpt":0.2416017652760389,"score_spread":0.2106956906874293,"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."}}