{"id":"W4248118815","doi":"10.32920/ryerson.14662845","title":"Data Analytics of Library Resources","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Computer science; Cluster analysis; Analytics; Task (project management); Resource (disambiguation); Usage data; Data science; Association rule learning; World Wide Web; Information retrieval; Visualization; Digital library; Data mining; Engineering; Artificial intelligence","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":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.002120677,0.0008958218,0.001004396,0.0147996,0.0007667552,0.004155575,0.001026088,0.0005222798,0.003364596],"category_scores_gemma":[0.01334269,0.0003607989,0.0009403125,0.02065878,0.000371983,0.002972369,0.001695826,0.0008941124,0.003491231],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001246153,"about_ca_system_score_gemma":0.001524127,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008817949,"about_ca_topic_score_gemma":0.007866075,"domain_scores_codex":[0.9953231,0.0008805723,0.0005588303,0.0007879178,0.00217262,0.0002769344],"domain_scores_gemma":[0.9861482,0.004641029,0.001766046,0.00240454,0.004416396,0.0006236869],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0008355675,0.0007409421,0.2283026,0.003035808,0.0007325787,0.0007542938,0.002499284,0.01921689,0.0146333,0.008499136,0.0725085,0.6482412],"study_design_scores_gemma":[0.00008962638,0.0005626521,0.4586736,0.001139706,0.0003968807,0.001383655,0.00614632,0.166609,0.05981734,0.02160417,0.283213,0.0003641499],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.441943,0.006088468,0.1342886,0.003409865,0.0003325048,0.001658318,0.3417649,0.02020634,0.05030793],"genre_scores_gemma":[0.6971002,0.002304163,0.1255317,0.0004458658,0.0002482249,0.000801844,0.1644021,0.0009200233,0.008245908],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9852004,"threshold_uncertainty_score":0.01753324,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1956933059625109,"score_gpt":0.3105674434897897,"score_spread":0.1148741375272788,"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."}}