{"id":"W2114175725","doi":"10.1109/tkde.2009.49","title":"Enhancing Learning Objects with an Ontology-Based Memory","year":2009,"lang":"en","type":"article","venue":"IEEE Transactions on Knowledge and Data Engineering","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":27,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Reusability; Learning object; Ontology; Semantic Web; Process (computing); Knowledge base; Artificial intelligence; Natural language processing; World Wide Web; Programming language","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":[0.001097899,0.0003121765,0.0003860597,0.0009362364,0.0007836079,0.002978392,0.001763621,0.000814438,0.00201338],"category_scores_gemma":[0.003409038,0.000340399,0.0006977285,0.0009684542,0.001112577,0.008829594,0.003842257,0.001094623,0.0008101422],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006360203,"about_ca_system_score_gemma":0.001371662,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003223082,"about_ca_topic_score_gemma":0.003901459,"domain_scores_codex":[0.9994169,0.00009352279,0.00008715405,0.0001069983,0.0002149697,0.00008044052],"domain_scores_gemma":[0.9985123,0.0003040167,0.0001597756,0.0006459436,0.0002441667,0.0001338072],"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":[0.0002186104,0.0007561985,0.004561213,0.000505617,0.0001273068,0.0005880402,0.003742196,0.01888233,0.0357227,0.2806393,0.008167138,0.6460894],"study_design_scores_gemma":[0.0001758751,0.0005053799,0.003597846,0.0003339277,0.0006041164,0.00158077,0.002884198,0.2454037,0.08920144,0.3155843,0.3399311,0.0001975169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08648232,0.000659201,0.8813296,0.001824863,0.0001912738,0.0002507865,0.0001924516,0.003725019,0.0253445],"genre_scores_gemma":[0.3993351,0.0007762984,0.5864334,0.0004033098,0.00007622405,0.0002337238,0.0004802952,0.0002413111,0.01202035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003223082,"threshold_uncertainty_score":0.006735444,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01977965045427822,"score_gpt":0.2566786341254655,"score_spread":0.2368989836711873,"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."}}