{"id":"W2092739591","doi":"10.1016/j.joi.2009.12.001","title":"Combining commercial citation indexes and open-access bibliographic databases to delimit highly interdisciplinary research fields for citation analysis","year":2010,"lang":"en","type":"article","venue":"Journal of Informetrics","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":29,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Stem Cell Network","keywords":"Citation; Scientometrics; Scopus; Computer science; Field (mathematics); Data science; Citation analysis; Bibliometrics; Information retrieval; Citation database; World Wide Web; MEDLINE; Political science; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["metaresearch","bibliometrics"],"category_scores_codex":[0.05869103,0.0001527991,0.0005735782,0.4840632,0.000734689,0.01132054,0.004837017,0.0001549404,0.00007730517],"category_scores_gemma":[0.1049412,0.0001094026,0.0002561279,0.5986164,0.0001834607,0.006110222,0.00438198,0.0009325778,0.000007934746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006017795,"about_ca_system_score_gemma":0.0003434872,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001738437,"about_ca_topic_score_gemma":0.0006686446,"domain_scores_codex":[0.9901093,0.0002062834,0.001614525,0.0003870071,0.007141712,0.0005411944],"domain_scores_gemma":[0.9600272,0.02160822,0.0009846956,0.0005705445,0.01610565,0.0007037154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004482073,0.0002250275,0.593486,0.00002018015,0.0002052818,0.000009253269,0.0008784445,0.0003244064,0.000417366,0.002281979,0.05933686,0.342367],"study_design_scores_gemma":[0.001301745,0.001599432,0.9647441,0.00002703627,0.00009713349,0.00001715307,0.002141383,0.005401949,0.0005542421,0.01255122,0.01132046,0.0002441664],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9036646,0.0001521048,0.09146789,0.002435349,0.0008869426,0.0004359367,0.00007825988,0.000005253984,0.0008736557],"genre_scores_gemma":[0.9831178,0.0003445689,0.01598169,0.0002786119,0.0001528181,0.00001622166,0.00001728489,0.000009859153,0.00008111616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3712581,"threshold_uncertainty_score":0.9897058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7928641188145871,"score_gpt":0.6879661796861452,"score_spread":0.1048979391284419,"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."}}