{"id":"W2924656839","doi":"10.1016/j.heliyon.2019.e01243","title":"Corrigendum to “Citation analysis of scientific categories” [Heliyon 3 (5) (May 2017) e00300]","year":2019,"lang":"en","type":"erratum","venue":"Heliyon","topic":"Legal, Health, Environmental and COVID-19 Challenges","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Citation; Mistake; Compromise; Bibliometrics; Computer science; Scientific literature; Citation analysis; Information retrieval; Library science; Social science; Sociology; Political science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch","bibliometrics","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003533183,0.002264183,0.001794615,0.0135701,0.001955957,0.006151925,0.00294003,0.00242924,0.7426757],"category_scores_gemma":[0.05663594,0.001165163,0.001763227,0.01491349,0.0009586666,0.003345711,0.004334886,0.002603903,0.4720561],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002980038,"about_ca_system_score_gemma":0.004477749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01237517,"about_ca_topic_score_gemma":0.01215962,"domain_scores_codex":[0.995904,0.0006150976,0.000699327,0.0005071764,0.001886594,0.0003877543],"domain_scores_gemma":[0.9560145,0.01219427,0.002663085,0.003001192,0.0243276,0.00179942],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000008102009,0.000004557445,0.00004614042,0.00008111693,0.000003697534,0.00001466207,0.000007881291,0.00001187407,0.00001943048,0.0002063774,0.9967986,0.002797581],"study_design_scores_gemma":[0.00003289733,0.0000171318,0.00220234,0.000422054,0.0000164967,0.00008506472,0.00005730966,0.0001009882,0.0001762456,0.001521208,0.9953349,0.00003331437],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"editorial","genre_gemma":"other","genre_scores_codex":[0.001127828,0.002647789,0.00883583,0.06117557,0.4722533,0.0004927852,0.330749,0.02087607,0.1018419],"genre_scores_gemma":[0.009988314,0.00614083,0.01163018,0.01886087,0.07186829,0.001215616,0.3386682,0.01666952,0.5249581],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.9964668,"threshold_uncertainty_score":0.3670418,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04656131611740794,"score_gpt":0.3107281954773308,"score_spread":0.2641668793599228,"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."}}