{"id":"W3024612087","doi":"10.3390/su12103997","title":"A Bibliometric Analysis on No-Show Research: Status, Hotspots, Trends and Outlook","year":2020,"lang":"en","type":"article","venue":"Sustainability","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Humanities and Social Science Fund of Ministry of Education of China; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Sustainability; Scope (computer science); Bibliometrics; Service (business); Regional science; Web of science; Institution; Political science; Position (finance); Business; Library science; Marketing; Computer science; Geography; Social science; Sociology; MEDLINE","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":["metaresearch","bibliometrics","sts","insufficient_payload"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.003679985,0.000158383,0.0003857283,0.03323347,0.001427864,0.00006513306,0.0001442589,0.0002368122,0.0009429508],"category_scores_gemma":[0.01522745,0.0001362633,0.00008930431,0.1840229,0.0001418831,0.0001861489,0.0001514627,0.001088009,0.00008529285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009302985,"about_ca_system_score_gemma":0.001567719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00160738,"about_ca_topic_score_gemma":0.0002413079,"domain_scores_codex":[0.99501,0.002306089,0.0005679125,0.0006447053,0.0005715584,0.0008997372],"domain_scores_gemma":[0.9935919,0.001165595,0.0001010706,0.0005235653,0.003942031,0.0006758313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002968812,0.0001632306,0.9244727,0.0006840696,0.0001060608,0.00001004946,0.007858709,0.001587552,0.000005355714,0.004224522,0.006411928,0.0541789],"study_design_scores_gemma":[0.0005520703,0.0004486765,0.9411276,0.00001356416,0.00006019018,7.107398e-8,0.005386841,0.02196609,0.000001505698,0.0004505626,0.02982283,0.0001700077],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9270095,0.0002627716,0.001708479,0.06388116,0.0001231675,0.001143285,0.00009287675,0.0001705421,0.005608265],"genre_scores_gemma":[0.9944172,0.0001828887,0.001119624,0.001357649,0.0001865694,0.000147401,0.0000790731,0.00002011539,0.002489489],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1507894,"threshold_uncertainty_score":0.9999703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1769485425485671,"score_gpt":0.5096127844642165,"score_spread":0.3326642419156494,"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."}}