{"id":"W2015148240","doi":"10.1108/00330331111107411","title":"The application of intelligent agents in libraries: a survey","year":2011,"lang":"en","type":"article","venue":"Program electronic library and information systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Intelligent agent; Digital library; Computer science; Context (archaeology); Architecture; Originality; Interface (matter); Agent architecture; Process (computing); Knowledge management; Variety (cybernetics); World Wide Web; 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":[0.000560637,0.00009653941,0.0001326368,0.0001194926,0.00009028039,0.0002858096,0.000427754,0.00005941707,0.000002498056],"category_scores_gemma":[0.00001324464,0.00006798474,0.00002585714,0.0005174091,0.00003084634,0.00988701,0.0000987639,0.00009346993,0.00001138532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001396801,"about_ca_system_score_gemma":0.00008693006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001357709,"about_ca_topic_score_gemma":0.000005363528,"domain_scores_codex":[0.9986987,0.0001692935,0.0006124428,0.0001174725,0.000159848,0.0002422553],"domain_scores_gemma":[0.9992239,0.0000775501,0.0003365791,0.0002893549,0.00002752062,0.00004502662],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002742403,0.00005485971,0.03001268,0.00009355317,0.00001780403,7.863035e-8,0.002564027,0.00001475284,0.000002686771,0.8594849,0.0001059703,0.1076213],"study_design_scores_gemma":[0.0008051057,0.0005718361,0.1968892,0.00009883389,0.000005456192,0.00001478292,0.0005869434,0.5009335,0.001870646,0.002844574,0.2950047,0.0003744655],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1650047,0.008678524,0.7791229,0.0004763779,0.001480475,0.01090662,0.0000266283,0.001198623,0.03310505],"genre_scores_gemma":[0.9986973,0.0004576641,0.0004410529,0.00004160932,0.00001329151,0.0002610328,0.00004054,0.000004217319,0.00004328531],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8566403,"threshold_uncertainty_score":0.7167844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03264599564042825,"score_gpt":0.2450136141541406,"score_spread":0.2123676185137123,"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."}}