{"id":"W2892058418","doi":"10.1007/978-3-030-00066-0_38","title":"Association Rule Based Clustering of Electronic Resources in University Digital Library","year":2018,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Recommender Systems and Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Cluster analysis; Computer science; Association rule learning; Digital library; Task (project management); Data mining; Resource (disambiguation); Analytics; Information retrieval; Cluster (spacecraft); Association (psychology); Data science; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005582378,0.0002515385,0.0003725005,0.0008409911,0.00007835321,0.0003543794,0.002067169,0.0002618971,0.00001909203],"category_scores_gemma":[0.00003202767,0.0002537496,0.00009350262,0.0005780886,0.0001550812,0.001094836,0.0009652408,0.0004280131,0.000007380417],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005172401,"about_ca_system_score_gemma":0.0003832049,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003318027,"about_ca_topic_score_gemma":0.0001093363,"domain_scores_codex":[0.9979033,0.00004813871,0.0003521325,0.0007344114,0.0005140702,0.000447942],"domain_scores_gemma":[0.9985103,0.0003376633,0.000384179,0.0006264306,0.00008119368,0.00006025343],"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":[0.00006929918,0.0002338958,0.02079808,0.0003872874,0.00008578201,0.0001904116,0.003749558,0.01428444,0.0001868739,0.01354493,0.001173387,0.945296],"study_design_scores_gemma":[0.0006348515,0.0005227716,0.001185224,0.00130533,0.000008369969,0.00001739982,0.000001126292,0.8456749,0.00286097,0.1114559,0.03533168,0.001001413],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0008194679,0.00009103656,0.9930133,0.0004246979,0.0003026082,0.0002049171,0.000009823042,0.000152935,0.004981252],"genre_scores_gemma":[0.7735163,0.00004237175,0.2235456,0.0004957903,0.0003417725,0.000002582587,0.00001259604,0.00004652398,0.001996452],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9442946,"threshold_uncertainty_score":0.9999915,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00818740504254354,"score_gpt":0.1940800350587164,"score_spread":0.1858926300161728,"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."}}