{"id":"W2612430136","doi":"10.1016/j.heliyon.2017.e00300","title":"Citation analysis of scientific categories","year":2017,"lang":"en","type":"article","venue":"Heliyon","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":117,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Polytechnique Montréal","funders":"","keywords":"Citation analysis; Citation; Bibliometrics; Data science; Management science; Computer science; Library science; Engineering","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":["metaresearch","bibliometrics","scholarly_communication"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.01547271,0.0000634044,0.000258542,0.08282517,0.0008116148,0.005699412,0.002331486,0.00005369917,0.0006241833],"category_scores_gemma":[0.04375732,0.00004380705,0.0001876372,0.1551689,0.0004575709,0.0007202626,0.0004181825,0.00007239413,0.0002247673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002846245,"about_ca_system_score_gemma":0.0001005982,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000850408,"about_ca_topic_score_gemma":0.0001772732,"domain_scores_codex":[0.9925956,0.00007908608,0.0004324995,0.000450902,0.00618918,0.000252753],"domain_scores_gemma":[0.9930304,0.00111084,0.0004636711,0.001563742,0.003684395,0.0001469886],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001245719,0.00007561713,0.8814479,0.00001188769,0.0001064999,0.000003465302,0.0003188672,0.0001012659,0.006622673,0.005738004,0.00178091,0.1037805],"study_design_scores_gemma":[0.0000979785,0.00002984087,0.9808281,0.000004095861,0.00004417051,1.7192e-7,0.0001686536,0.003494626,0.004764372,0.003311875,0.007193685,0.00006242489],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9843296,0.0006037446,0.002389425,0.0003883713,0.0006888342,0.00006662613,0.00003420935,0.00000734643,0.01149182],"genre_scores_gemma":[0.9941343,0.000109963,0.0003415876,0.00001058733,0.00002128099,0.000002482958,0.000006738941,0.000002730452,0.005370296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1037181,"threshold_uncertainty_score":0.9953328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6441124752737633,"score_gpt":0.5944176859984289,"score_spread":0.04969478927533433,"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."}}