{"id":"W2982283208","doi":"10.5539/mas.v13n11p76","title":"Fuzzy Logic System for Retrieval of Information in Electronic Libraries","year":2019,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Educational Methods and Technology","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Intranet; Relevance (law); Information retrieval; Rank (graph theory); Fuzzy logic; Search engine indexing; Digital library; Flexibility (engineering); Information system; Data mining; The Internet; Database; World Wide Web; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009724434,0.00006329193,0.0001250341,0.0002872539,0.00005832162,0.00007408152,0.001046671,0.00004985083,7.372386e-7],"category_scores_gemma":[0.00005552357,0.00005638305,0.00001804743,0.0009435485,0.0001301111,0.0008447588,0.0001618436,0.0000825059,0.00001435039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001021486,"about_ca_system_score_gemma":0.0004740624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003404613,"about_ca_topic_score_gemma":5.533778e-7,"domain_scores_codex":[0.9990242,0.00000934732,0.0002147879,0.0002216012,0.0002456708,0.0002843786],"domain_scores_gemma":[0.9993246,0.00009794324,0.000121405,0.0003570177,0.00007348928,0.00002559804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000008797715,0.000009583167,0.0001333357,0.00003140678,6.4732e-7,1.994819e-8,0.0003267743,0.0001377228,0.03929805,0.9528925,0.000002164295,0.007158938],"study_design_scores_gemma":[0.0002759079,0.00008432467,0.001185041,0.00001263851,9.721058e-7,0.000003074759,0.0001817885,0.1827271,0.05863329,0.7566047,0.0001863951,0.0001047981],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1236792,0.00003937879,0.8695239,0.0002746493,0.0001684602,0.0003785717,0.000001073046,0.00007318431,0.005861538],"genre_scores_gemma":[0.8664705,0.000001328883,0.1334221,0.00006389301,0.000005577187,0.00002196818,7.624582e-7,0.000001633151,0.00001220182],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7427913,"threshold_uncertainty_score":0.2299234,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01377024952839181,"score_gpt":0.2549802211438251,"score_spread":0.2412099716154333,"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."}}