{"id":"W2120520504","doi":"10.5815/ijmecs.2015.01.01","title":"Semantic Question Generation Using Artificial Immunity","year":2015,"lang":"en","type":"article","venue":"International Journal of Modern Education and Computer Science","topic":"Topic Modeling","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Sentence; Preprocessor; Artificial intelligence; Natural language processing; Classifier (UML); Set (abstract data type); Test set; Matching (statistics); Semantic role labeling","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":[0.001271317,0.0005787498,0.0005107618,0.001138743,0.0003491715,0.001226372,0.001098309,0.001144383,0.002580542],"category_scores_gemma":[0.004842102,0.0002571905,0.001266843,0.0004605559,0.000567575,0.002405534,0.0008829529,0.0009955728,0.0009841167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007047612,"about_ca_system_score_gemma":0.0005774719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001339916,"about_ca_topic_score_gemma":0.0009548222,"domain_scores_codex":[0.9987726,0.000486331,0.00007405348,0.0003554516,0.0002275187,0.00008404269],"domain_scores_gemma":[0.9980146,0.001039661,0.0001783771,0.0002104876,0.0004889459,0.00006792515],"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":[0.0005929013,0.001049231,0.01177388,0.0005987597,0.0002674881,0.0007763108,0.002110752,0.169982,0.07086907,0.06869002,0.01402222,0.6592674],"study_design_scores_gemma":[0.00002078128,0.0001327395,0.001070941,0.00002016714,0.0000444232,0.00013626,0.0001027514,0.9589101,0.008251981,0.02703722,0.004251845,0.00002073998],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0923354,0.0004324156,0.8953041,0.001215098,0.0001648845,0.0003457346,0.0002755601,0.003043624,0.00688325],"genre_scores_gemma":[0.7824267,0.0002080593,0.2108939,0.0005741646,0.0001241134,0.00028519,0.001015679,0.0001076069,0.004364493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002580542,"threshold_uncertainty_score":0.008632839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08427210239310003,"score_gpt":0.3489781839158917,"score_spread":0.2647060815227917,"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."}}