{"id":"W4409796353","doi":"10.1109/southeastcon56624.2025.10971611","title":"ChessMoveLLM: Large Language Models for Chess Next Move Prediction","year":2025,"lang":"en","type":"article","venue":"","topic":"Sports Analytics and Performance","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001145918,0.003010114,0.001085612,0.001886349,0.000735083,0.00163189,0.004240413,0.002488778,0.0137514],"category_scores_gemma":[0.00442668,0.0008434902,0.002283954,0.001456426,0.0003936043,0.002831471,0.001482343,0.003528533,0.007783927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001914382,"about_ca_system_score_gemma":0.001731202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03680901,"about_ca_topic_score_gemma":0.06356641,"domain_scores_codex":[0.9993958,0.0001430538,0.00004454552,0.0002496978,0.00009185499,0.00007502679],"domain_scores_gemma":[0.9987009,0.0008040957,0.00006556863,0.0002013011,0.0001439155,0.00008414796],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001151472,0.001047538,0.006792753,0.0008544248,0.0005626961,0.0005046094,0.0001427273,0.377467,0.003180546,0.006086207,0.185215,0.4169951],"study_design_scores_gemma":[0.00007501417,0.00007219927,0.0004439656,0.00003362452,0.00003151028,0.00004867398,0.0000325382,0.9866703,0.001288353,0.004273242,0.007007537,0.00002304381],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1431047,0.008493241,0.5138975,0.003668507,0.001619064,0.001272651,0.1164924,0.1939531,0.01749876],"genre_scores_gemma":[0.3541473,0.001415652,0.4450812,0.00135167,0.0003132336,0.001592693,0.1780627,0.002432467,0.01560316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03680901,"threshold_uncertainty_score":0.07318956,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03020086995546571,"score_gpt":0.2358309623613195,"score_spread":0.2056300924058538,"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."}}