{"id":"W1990520274","doi":"10.1108/07378831011096196","title":"Artificially intelligent conversational agents in libraries","year":2010,"lang":"en","type":"article","venue":"Library Hi Tech","topic":"AI in Service Interactions","field":"Computer Science","cited_by":104,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Computer science; Conversation; Originality; World Wide Web; Implementation; Argument (complex analysis); Knowledge management; Human–computer interaction; Sociology; Qualitative research","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.003884966,0.0004996584,0.0002954356,0.001499351,0.004274641,0.007261014,0.001460314,0.001137322,0.002700407],"category_scores_gemma":[0.01183459,0.0003843298,0.0003248202,0.001149953,0.006863187,0.004446113,0.00433337,0.0008657669,0.0007398243],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009443647,"about_ca_system_score_gemma":0.009455363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09956086,"about_ca_topic_score_gemma":0.07211809,"domain_scores_codex":[0.993544,0.004689996,0.000234329,0.0003776959,0.0008057043,0.0003483272],"domain_scores_gemma":[0.9931132,0.003986143,0.0008789977,0.0005914371,0.0009140705,0.0005161125],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006288135,0.0002500296,0.02451515,0.001994574,0.0001086133,0.001279502,0.1921586,0.02483891,0.0175781,0.4448487,0.01003136,0.2817677],"study_design_scores_gemma":[0.000144278,0.000336715,0.01457753,0.001142173,0.0002176952,0.001068008,0.08358189,0.06855239,0.01804931,0.1266685,0.6851862,0.0004752756],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4371117,0.007671409,0.295725,0.009114364,0.0002076725,0.0009622975,0.0003862263,0.003917087,0.2449042],"genre_scores_gemma":[0.9441357,0.0009251916,0.04414202,0.0004828051,0.00004265527,0.0002297322,0.0001173757,0.0001025141,0.009822032],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09956086,"threshold_uncertainty_score":0.1979627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02667571733708074,"score_gpt":0.2652437038154152,"score_spread":0.2385679864783344,"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."}}