{"id":"W3211368050","doi":"10.48550/arxiv.2208.01366","title":"Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess","year":2022,"lang":"en","type":"article","venue":"arXiv (Cornell University)","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Stylometry; Style (visual arts); Computer science; Artificial intelligence; Art; Literature","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0006699779,0.0001406713,0.0002668714,0.0007905078,0.0003376946,0.00005437909,0.0004426701,0.00004675144,0.001193576],"category_scores_gemma":[0.00002817145,0.0002059224,0.0001031025,0.001421405,0.00003149574,0.0004509568,0.0003885458,0.0003812795,0.00005272353],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003070079,"about_ca_system_score_gemma":0.00002134453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002291001,"about_ca_topic_score_gemma":0.0001611184,"domain_scores_codex":[0.9987345,0.00001349688,0.0003395885,0.0005214915,0.00004223187,0.0003486675],"domain_scores_gemma":[0.9992804,0.0000731264,0.0002473382,0.0003177147,0.00001600176,0.00006541672],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003784084,0.0001287742,0.8418238,0.000007612919,0.00002148427,0.0001917158,0.0006140569,0.1242646,0.000001928125,0.02998314,0.00002603631,0.002899101],"study_design_scores_gemma":[0.002286862,0.0003261395,0.5982103,0.00007934293,0.00004705475,0.0000211606,0.008384508,0.3589146,0.00003269975,0.01961342,0.01071913,0.001364827],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9948252,0.0001560674,0.00293443,0.000008172854,0.0003800682,0.00009088182,0.00005693312,0.00003394777,0.001514323],"genre_scores_gemma":[0.9994234,0.00005474014,0.0001137219,0.00004903357,0.00004094431,0.000003131822,0.000006194343,0.00002211011,0.0002866787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2436135,"threshold_uncertainty_score":0.9997194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1887474064130984,"score_gpt":0.2059643180096651,"score_spread":0.01721691159656669,"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."}}