{"id":"W4220766722","doi":"10.1371/journal.pone.0265929","title":"Multiple correspondence analysis as a tool for examining Nobel Prize data from 1901 to 2018","year":2022,"lang":"en","type":"article","venue":"PLoS ONE","topic":"Complex Network Analysis Techniques","field":"Physics and Astronomy","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nobel laureate; Residence; Contingency table; Categorical variable; Demography; Classics; Library science; Psychology; History; Sociology; Philosophy; Mathematics; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.008775425,0.0005330162,0.0007346124,0.01183454,0.001779122,0.001562154,0.0009967316,0.0006736558,0.01061112],"category_scores_gemma":[0.05614081,0.0002891451,0.0006276294,0.01950371,0.0007951319,0.001638684,0.00311025,0.001416635,0.001477023],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001247616,"about_ca_system_score_gemma":0.001766408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008044713,"about_ca_topic_score_gemma":0.008975502,"domain_scores_codex":[0.9870863,0.006931613,0.000870571,0.001791368,0.002649785,0.0006704028],"domain_scores_gemma":[0.9352074,0.04312064,0.01041059,0.005052075,0.005096887,0.001112408],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001114751,0.0004130329,0.7423206,0.0008677479,0.001064626,0.001150475,0.01192691,0.01213731,0.002378575,0.04819162,0.03579194,0.1426425],"study_design_scores_gemma":[0.00007296272,0.0005901659,0.7924382,0.00029622,0.0002841957,0.001002326,0.01225822,0.0364346,0.003807094,0.0337817,0.1188354,0.0001989406],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7980082,0.0007928009,0.1094218,0.001148779,0.0003175842,0.0007012336,0.05779525,0.0008540684,0.03096031],"genre_scores_gemma":[0.9232022,0.0002490849,0.04788247,0.00009605368,0.0001190061,0.002346417,0.02145871,0.0001696601,0.004476444],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9881654,"threshold_uncertainty_score":0.04640943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1211870689941406,"score_gpt":0.2990949896268842,"score_spread":0.1779079206327437,"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."}}