{"id":"W2950927168","doi":"10.48550/arxiv.0808.2517","title":"Why it has become more difficult to predict Nobel Prize winners: a bibliometric analysis of Nominees and Winners of the Chemistry and Physics Prizes (1901-2007)","year":2008,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"scientometrics and bibliometrics research","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Citation; Hierarchy; Attribution; Field (mathematics); Fragmentation (computing); Centrality; Scientific progress; Data science; Political science; Epistemology; Psychology; Library science; Computer science; Mathematics; Statistics; Law; Philosophy; Social 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":[{"model":"gemma","categories":["bibliometrics","metaresearch"],"domain":"evaluation","study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":["bibliometrics"],"domain":null,"study_design":"observational","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"high","status":"direct model label, unvalidated"}],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","bibliometrics"],"consensus_categories":["bibliometrics"],"category_scores_codex":[0.005395651,0.0005548934,0.001684609,0.1961787,0.0002932899,0.0009591489,0.003802735,0.0004815907,0.0001169852],"category_scores_gemma":[0.02732534,0.0003566263,0.0006464553,0.7433059,0.001357823,0.0003509719,0.005901036,0.0007857502,0.000006139216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001266774,"about_ca_system_score_gemma":0.0004408375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006007114,"about_ca_topic_score_gemma":0.00002727559,"domain_scores_codex":[0.9881251,0.0001927619,0.001685831,0.001747046,0.00749681,0.000752449],"domain_scores_gemma":[0.98768,0.003700844,0.001352631,0.002275857,0.00435159,0.0006390812],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004463275,0.0002747626,0.9729198,0.0001415518,0.0007821749,0.000006836059,0.0007894989,0.00135703,0.005464848,0.000002194145,0.01076285,0.007453837],"study_design_scores_gemma":[0.0005483479,0.00009457612,0.984043,0.00008729522,0.0003903037,0.000005046575,0.0005064869,0.006725411,0.003766255,0.00006744771,0.00336497,0.0004008848],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927326,0.0018896,0.0007876637,0.002439903,0.0003158532,0.0006555095,0.0006640556,0.00002367538,0.000491095],"genre_scores_gemma":[0.9953264,0.002674169,0.0009047172,0.00031249,0.000140594,0.00003695783,0.00003841215,0.00003181403,0.0005344744],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5471272,"threshold_uncertainty_score":0.9998886,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3440724253615619,"score_gpt":0.4528800998423715,"score_spread":0.1088076744808096,"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."}}