{"id":"W2112327920","doi":"10.2991/eusflat.2013.121","title":"Fuzziness and Semantic Web Technologies in Personalized eLearning","year":2013,"lang":"en","type":"article","venue":"","topic":"Semantic Web and Ontologies","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Personalization; Computer science; Ontology; Semantic Web; Component (thermodynamics); Point (geometry); World Wide Web; Architecture; Selection (genetic algorithm); Human–computer interaction; Artificial intelligence","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":[0.0001219426,0.00009185115,0.0001484544,0.000145193,0.00004633106,0.0001533796,0.0004112011,0.00006389624,0.00002972769],"category_scores_gemma":[0.0001185239,0.00006733694,0.00001751713,0.0002778604,0.00009481359,0.0004032848,0.0002871142,0.0001044078,0.00007699197],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009247221,"about_ca_system_score_gemma":0.00001918629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001504499,"about_ca_topic_score_gemma":0.00006223058,"domain_scores_codex":[0.9992728,0.00002484848,0.0001216655,0.0002522702,0.00009791042,0.0002304994],"domain_scores_gemma":[0.9995896,0.0001018483,0.00002841305,0.0002334794,0.00002793917,0.00001871371],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.000004940264,0.00008055872,0.3564306,0.00009206153,0.00002304083,0.0000710404,0.001824577,0.00001642047,0.01142729,0.3047741,0.002228641,0.3230267],"study_design_scores_gemma":[0.002685243,0.0001986495,0.4566993,0.0001698123,0.00001018336,0.0002186179,0.01545707,0.445683,0.004977575,0.06668677,0.006092072,0.001121693],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9595027,0.0006629021,0.02645921,0.007347188,0.0000883309,0.0001661431,5.10654e-8,0.000906228,0.004867225],"genre_scores_gemma":[0.9787649,0.00009184827,0.02042044,0.00009872455,0.000004834591,0.00002117729,1.179693e-7,0.000003101828,0.0005948319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4456666,"threshold_uncertainty_score":0.2745921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01164283995344324,"score_gpt":0.2253957734661094,"score_spread":0.2137529335126661,"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."}}