{"id":"W2953936633","doi":"10.7152/acro.v29i1.15463","title":"Examining Communities in the Transdisciplinary Area of Cognitive Science: Automatic Classification for Examining Communities in the Web of Science Using Unsupervised Clustering Methods","year":2019,"lang":"en","type":"article","venue":"Advances in Classification Research Online","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Scopus; Subject (documents); Cluster analysis; Scope (computer science); Domain (mathematical analysis); Data science; Web of science; Information retrieval; Cognition; World Wide Web; Artificial intelligence; MEDLINE; Psychology","routes":{"ca_aff":true,"ca_fund":false,"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":["sts"],"consensus_categories":[],"category_scores_codex":[0.01514895,0.0001203437,0.000236044,0.000656169,0.0002954703,0.00004215309,0.00146014,0.00009842247,0.000005057195],"category_scores_gemma":[0.00181381,0.00008329224,0.00002842411,0.001854854,0.005495085,0.00006364553,0.0002129109,0.0004167742,1.417006e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006805967,"about_ca_system_score_gemma":0.0005839188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000066633,"about_ca_topic_score_gemma":0.0007803902,"domain_scores_codex":[0.9967865,0.001347563,0.0005835354,0.0002640024,0.0006156095,0.0004027738],"domain_scores_gemma":[0.9949507,0.003787984,0.0001909398,0.0006268367,0.0004161354,0.00002746874],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002372358,0.0006443431,0.03674988,0.0004929233,0.00001060047,8.150719e-7,0.05756424,0.0009844395,0.680694,0.0006904725,0.000002209881,0.2219288],"study_design_scores_gemma":[0.0007167757,0.0005019368,0.09817834,0.0006473091,0.000005276532,0.000004324479,0.6027795,0.292239,0.004381338,0.0003034958,0.0001400644,0.0001026035],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9919142,0.001176204,0.00544841,0.0002484996,0.00005751247,0.0006681613,0.00004003376,0.000005612954,0.0004413444],"genre_scores_gemma":[0.9785199,0.000839676,0.02035601,0.00003617915,0.00001647831,0.0001213056,0.00009589153,0.000008757818,0.000005783519],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6763127,"threshold_uncertainty_score":0.9972114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3714567148061632,"score_gpt":0.522537251833423,"score_spread":0.1510805370272599,"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."}}