{"id":"W4384079123","doi":"10.1007/978-3-031-31772-9_13","title":"Predicting Olympic Success by Regression Modeling in Sport - An Analysis of the Beginning of the 21st Century","year":2023,"lang":"en","type":"book-chapter","venue":"Advances in intelligent systems and computing","topic":"Sports Performance and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Sport Centre Pacific; University of Calgary","funders":"","keywords":"Medal; Competition (biology); Athletes; Predictive power; Competitor analysis; Regression analysis; Set (abstract data type); Political science; Psychology; Computer science; History; Statistics; Marketing; Mathematics; Business; Medicine; Physical therapy","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":[0.00132472,0.000674291,0.0005139567,0.001009361,0.0002075882,0.001362918,0.00081626,0.0004895467,0.003437466],"category_scores_gemma":[0.004750734,0.0002417315,0.0004696789,0.001887731,0.0004737127,0.001185119,0.0005626603,0.001072979,0.0009363542],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007843303,"about_ca_system_score_gemma":0.0005912436,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01327684,"about_ca_topic_score_gemma":0.02278984,"domain_scores_codex":[0.9996799,0.000140393,0.00001375893,0.00006306633,0.00007729318,0.00002563862],"domain_scores_gemma":[0.9981698,0.001430732,0.0001438431,0.00006347524,0.0001543314,0.00003779797],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001619621,0.0001749251,0.149719,0.0004351877,0.0003307108,0.000226873,0.0007937705,0.1918518,0.000773867,0.1431138,0.02686557,0.4855525],"study_design_scores_gemma":[0.00001306103,0.00014491,0.114426,0.000413495,0.0001032625,0.0002298779,0.000813715,0.7223442,0.0009241745,0.1235262,0.03698751,0.00007362019],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4807298,0.07128637,0.2732503,0.0144448,0.0009733316,0.00007551293,0.003327735,0.0009689272,0.1549432],"genre_scores_gemma":[0.8922513,0.02180913,0.04329321,0.0003818065,0.0004678884,0.00005185153,0.001962868,0.0002564416,0.03952548],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01327684,"threshold_uncertainty_score":0.02639914,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02666803395680185,"score_gpt":0.2934593913373713,"score_spread":0.2667913573805695,"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."}}