{"id":"W1970372549","doi":"10.1371/journal.pone.0122565","title":"Long-Distance Interdisciplinarity Leads to Higher Scientific Impact","year":2015,"lang":"en","type":"article","venue":"PLoS ONE","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":208,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Montréal","funders":"Social Sciences and Humanities Research Council of Canada; Canada Research Chairs","keywords":"Citation; Publication; Discipline; Citation analysis; Value (mathematics); Citation impact; Sociology; Library science; Data science; Space (punctuation); Scientific literature; Impact factor; Social science; Engineering ethics; Epistemology; Computer science; Political science; Biology; Law; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.006772856,0.0004991324,0.001252045,0.01232844,0.002025689,0.009619241,0.0006950095,0.001384935,0.007207396],"category_scores_gemma":[0.04841962,0.000240094,0.0007313291,0.02173878,0.002175549,0.005070765,0.007074445,0.001403849,0.00203557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001459523,"about_ca_system_score_gemma":0.001249459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009641296,"about_ca_topic_score_gemma":0.001346408,"domain_scores_codex":[0.9912629,0.001743176,0.0007404675,0.0009539083,0.004620638,0.0006790247],"domain_scores_gemma":[0.9381414,0.02448273,0.01694697,0.0045802,0.009718623,0.006130166],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002456639,0.0002740284,0.8460233,0.0009390449,0.0009546559,0.000566924,0.004097579,0.002632335,0.004366761,0.01782834,0.00200827,0.1200632],"study_design_scores_gemma":[0.00004588807,0.0002964603,0.925051,0.0001978294,0.0003306573,0.0007446214,0.008659533,0.001820938,0.002918855,0.04522729,0.01463468,0.00007213901],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9456339,0.005876011,0.004629619,0.001596875,0.00008640973,0.00005031302,0.0004561888,0.0001305886,0.04153989],"genre_scores_gemma":[0.9969859,0.000969763,0.0008502366,0.00004464318,0.00007287581,0.00001861385,0.0001619357,0.00001790486,0.0008782569],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9932271,"threshold_uncertainty_score":0.03581876,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7704361682673871,"score_gpt":0.5791235230802817,"score_spread":0.1913126451871054,"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."}}