{"id":"W3084756338","doi":"10.22452/mjlis.vol25no2.4","title":"Bibliometric mapping of top papers in Library and Information Science based on the Essential Science Indicators Database","year":2020,"lang":"en","type":"article","venue":"Malaysian Journal of Library & Information Science","topic":"Diverse Approaches in Healthcare and Education Studies","field":"Medicine","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Library science; China; Web of science; Field (mathematics); Subject (documents); Informatics; Information science; Computer science; Visualization; World Wide Web; Data science; Political science; MEDLINE; Data mining","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.001849415,0.0001046225,0.0001981044,0.0290315,0.0003554575,0.000408388,0.0007467962,0.00002533102,0.00009930572],"category_scores_gemma":[0.001259715,0.00007096829,0.00003954583,0.0945036,0.002147429,0.05510783,0.0002886111,0.0002704864,0.000007252294],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003368068,"about_ca_system_score_gemma":0.003165891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000011051,"about_ca_topic_score_gemma":4.589219e-9,"domain_scores_codex":[0.9972123,0.00002874852,0.0007955374,0.0001285142,0.00155982,0.0002750698],"domain_scores_gemma":[0.9983971,0.0001459131,0.0007007834,0.0002204991,0.0001723757,0.0003633889],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003811955,0.0001202254,0.8729552,0.0006087347,0.00001381218,0.000008654315,0.02373611,0.0006745537,0.002844095,0.03348009,0.0006474594,0.06452987],"study_design_scores_gemma":[0.0008937107,0.0004718474,0.9320304,0.0004070251,0.00001612299,0.00004919795,0.02278075,0.01472995,0.02077752,0.00002741211,0.007643156,0.0001729093],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9522901,0.00003185702,0.0001453337,0.01835009,0.0002148272,0.0002493841,0.00001095779,0.00001642381,0.02869098],"genre_scores_gemma":[0.9904788,0.0002475993,0.002662326,0.006550936,0.00004780517,0.000002945114,0.000004561113,0.000002729266,0.000002352137],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0654721,"threshold_uncertainty_score":0.9819736,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03749448456240843,"score_gpt":0.2840670053510015,"score_spread":0.2465725207885931,"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."}}