{"id":"W3115533035","doi":"10.1016/j.joi.2020.101124","title":"Exploring the interdisciplinarity patterns of highly cited papers","year":2020,"lang":"en","type":"article","venue":"Journal of Informetrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":56,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Université de Montréal","funders":"National Office for Philosophy and Social Sciences","keywords":"Computer science; Data science; Bibliometrics; Information retrieval; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":["metaresearch","bibliometrics"],"domain":"methods","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"design_other","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.003612443,0.0002334315,0.0004420954,0.01789994,0.001063877,0.004853471,0.0008072689,0.0007593944,0.003908975],"category_scores_gemma":[0.04324429,0.0001771643,0.0005274108,0.02736738,0.0004844472,0.002572994,0.001660686,0.0006234801,0.00100397],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005151518,"about_ca_system_score_gemma":0.0007553541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001878001,"about_ca_topic_score_gemma":0.003428087,"domain_scores_codex":[0.996595,0.0007385743,0.000638815,0.000616551,0.001176985,0.0002341535],"domain_scores_gemma":[0.9445987,0.03336598,0.009000393,0.002812237,0.008634342,0.001588321],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002924378,0.0001545183,0.8815221,0.0005476995,0.0006582657,0.0005189207,0.004890432,0.0006721693,0.007615804,0.004430407,0.001729852,0.09696742],"study_design_scores_gemma":[0.00002821042,0.0001387784,0.9659827,0.0001260802,0.0003002876,0.0008349448,0.007134762,0.004870483,0.002953258,0.01007536,0.007510495,0.0000446983],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9879591,0.001689257,0.002203598,0.0003654894,0.00003675398,0.00002849552,0.00134053,0.00004962532,0.006327087],"genre_scores_gemma":[0.9953138,0.0005520018,0.001743992,0.00002764969,0.000063782,0.00001956932,0.001111283,0.00002406769,0.001143797],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9963875,"threshold_uncertainty_score":0.01910466,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7915538673463653,"score_gpt":0.540354756721656,"score_spread":0.2511991106247093,"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."}}