{"id":"W2575833913","doi":"","title":"A Probabilistic Model for Knowledge Component Naming.","year":2015,"lang":"en","type":"article","venue":"NPARC","topic":"Intelligent Tutoring Systems and Adaptive Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"National Research Council Canada","funders":"","keywords":"Computer science; Probabilistic logic; Discriminative model; Artificial intelligence; Focus (optics); Matrix decomposition; Cluster analysis; Non-negative matrix factorization; Simple (philosophy); Machine learning; Topic model; Probabilistic latent semantic analysis; Component (thermodynamics); Natural language processing; Data mining","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.005874329,0.001514426,0.001626572,0.004024966,0.001137448,0.003755005,0.005298214,0.003690045,0.01161606],"category_scores_gemma":[0.02019067,0.00133569,0.002796699,0.00411403,0.00243304,0.00600099,0.002190778,0.00350537,0.004395789],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002825734,"about_ca_system_score_gemma":0.001967992,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144617,"about_ca_topic_score_gemma":0.01574719,"domain_scores_codex":[0.996352,0.001262354,0.0002484458,0.001224396,0.000684463,0.0002282709],"domain_scores_gemma":[0.9871818,0.009645274,0.001065145,0.0008347504,0.0009837827,0.0002892313],"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":[0.0004075415,0.0002886605,0.009715946,0.0006773183,0.000391977,0.0005700372,0.0008434395,0.420424,0.002439788,0.3875522,0.01530425,0.1613848],"study_design_scores_gemma":[0.00003355193,0.0000395579,0.0007385181,0.00003099119,0.00004465822,0.0002088392,0.00003386572,0.8440025,0.0002668899,0.150903,0.00366168,0.00003589847],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008039629,0.0007059452,0.984473,0.001092911,0.00009000158,0.0002326633,0.001986092,0.0007615703,0.002618125],"genre_scores_gemma":[0.4755092,0.001919356,0.4895146,0.0007826722,0.0005739689,0.002294672,0.005977353,0.0003939953,0.02303413],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0144617,"threshold_uncertainty_score":0.03885955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09391874428230981,"score_gpt":0.2912449094317262,"score_spread":0.1973261651494163,"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."}}