{"id":"W2139863663","doi":"10.1109/icalt.2003.1215162","title":"Developing a schema for learning object based on object oriented model of object inheritance","year":2004,"lang":"en","type":"article","venue":"","topic":"Open Education and E-Learning","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Computer science; Inheritance (genetic algorithm); Learning object; Object model; Schema (genetic algorithms); XML; CLIPS; Object (grammar); Object-oriented programming; Artificial intelligence; Programming language; XML Schema (W3C); Markup language; Natural language processing; Information retrieval; Document Structure Description; World Wide Web; Document type definition","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":[0.0005350606,0.0001985781,0.0002581384,0.0002030942,0.0002868812,0.0001158419,0.0005290998,0.00007809048,0.00001751497],"category_scores_gemma":[0.0004905522,0.0001882997,0.0001083375,0.0007428385,0.00003391197,0.0004194093,0.00009554967,0.0002531024,0.00003013473],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002079318,"about_ca_system_score_gemma":0.001085519,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004899599,"about_ca_topic_score_gemma":0.00001906148,"domain_scores_codex":[0.9983805,0.00006385727,0.0003468174,0.0004971654,0.0003246561,0.0003870222],"domain_scores_gemma":[0.9989272,0.0001811176,0.000182484,0.0003530521,0.0002662106,0.00008994447],"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":[0.0000724058,0.0002172334,0.0009018275,0.0001219495,0.00002809684,0.00000186661,0.003679842,0.6147365,0.005771597,0.3676281,0.0001182522,0.006722325],"study_design_scores_gemma":[0.001442088,0.0003388191,0.0004241426,0.0002367774,0.000006227294,0.00000300929,0.00119231,0.9494469,0.04376009,0.001717066,0.001044662,0.000387969],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05291514,0.00001448504,0.9297321,0.001079795,0.0002097527,0.0003238923,5.758744e-7,0.0002371346,0.01548712],"genre_scores_gemma":[0.6140984,0.00000320112,0.3847251,0.0004475056,0.00001950857,0.00003979439,0.00000396835,0.00001429241,0.0006483122],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.5611832,"threshold_uncertainty_score":0.767864,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03784606229311457,"score_gpt":0.2938507501960134,"score_spread":0.2560046879028988,"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."}}