{"id":"W2982097438","doi":"10.3233/isu-190065","title":"Artificial intelligence in academic libraries: An environmental scan","year":2019,"lang":"en","type":"article","venue":"Information Services & Use","topic":"AI in Service Interactions","field":"Computer Science","cited_by":133,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Academic library; Computer science; Data science; Business; Information retrieval; Library science","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":"codex-gemma-dda1882f352a","candidate_categories":["scholarly_communication","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001764589,0.000147115,0.0001221303,0.0002928722,0.00007074085,0.0006280741,0.001278271,0.0001250555,0.0002479512],"category_scores_gemma":[0.000005988265,0.0001534212,0.00003294454,0.0004228282,0.00002180844,0.03783518,0.0003514201,0.0003341369,0.004037019],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001012994,"about_ca_system_score_gemma":0.00004731895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003124878,"about_ca_topic_score_gemma":0.0001359915,"domain_scores_codex":[0.9985526,0.00004592496,0.0006086634,0.0002008692,0.0003315222,0.0002603856],"domain_scores_gemma":[0.9989356,0.0001036148,0.0002220494,0.0006110935,0.00002708932,0.0001005029],"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":[0.0001516307,0.0003599303,0.1167865,0.0002759076,0.00004370254,0.000008920189,0.1871834,0.03167219,0.003006901,0.1812917,0.0001443738,0.479075],"study_design_scores_gemma":[0.0001156277,0.0001110904,0.0416577,0.00009176083,0.000004127235,0.00002330534,0.008524973,0.9132419,0.003937254,0.00870578,0.02312964,0.0004568459],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9709306,0.00001415578,0.02645355,0.0005394198,0.000575207,0.0002981465,0.00001547887,0.0001941483,0.0009793432],"genre_scores_gemma":[0.9890715,0.00001954884,0.007420407,0.003324983,0.0000396088,0.000018542,0.00006905643,0.000006845319,0.00002952732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8815697,"threshold_uncertainty_score":0.9967384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02269263748107633,"score_gpt":0.2677144477277207,"score_spread":0.2450218102466444,"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."}}