{"id":"W7049342643","doi":"","title":"Multi-way correspondence analysis approach to examine Nobel Prize Data from 1901 to 2018","year":2021,"lang":"en","type":"article","venue":"Figshare","topic":"Advanced Measurement and Metrology Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Correspondence analysis; Contingency table; Categorical variable; Association (psychology); Nationality; Multiple correspondence analysis; Multivariate analysis","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":["metaresearch","bibliometrics"],"consensus_categories":[],"category_scores_codex":[0.009177416,0.0006382847,0.001037717,0.00908102,0.001791518,0.002007436,0.001116427,0.0008723451,0.009589496],"category_scores_gemma":[0.04270067,0.0002855413,0.001347323,0.01346741,0.0007575625,0.001450029,0.002722734,0.001634561,0.002007523],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001326684,"about_ca_system_score_gemma":0.002003927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008425461,"about_ca_topic_score_gemma":0.008020195,"domain_scores_codex":[0.9864136,0.005841214,0.001084875,0.002368292,0.003368918,0.0009231034],"domain_scores_gemma":[0.962639,0.02065349,0.005577062,0.003967304,0.006303865,0.000859173],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001211696,0.0005367859,0.7968468,0.000668059,0.001560241,0.001061116,0.01090864,0.007649747,0.001424906,0.01876399,0.02760499,0.131763],"study_design_scores_gemma":[0.00006098853,0.000723713,0.8763207,0.0002432977,0.0003837765,0.0005774032,0.01708619,0.02664604,0.002494017,0.01226484,0.0629824,0.0002165983],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8918274,0.0009129967,0.05814771,0.0008097475,0.0007674637,0.0009595437,0.02599211,0.0005113092,0.02007173],"genre_scores_gemma":[0.9620527,0.0002284048,0.01954575,0.0001030287,0.0001688316,0.002125495,0.0105624,0.0001421435,0.005071145],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.990919,"threshold_uncertainty_score":0.04853541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.167871225505988,"score_gpt":0.3073281141530798,"score_spread":0.1394568886470917,"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."}}