{"id":"W2247922307","doi":"","title":"Curling: Chess on ice","year":2010,"lang":"en","type":"article","venue":"Optometry Times","topic":"Winter Sports Injuries and Performance","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Curling; Artificial intelligence; Computer science; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001252449,0.0001022361,0.000161506,0.0001358174,0.00005356858,0.0000197709,0.00009213718,0.00008041182,0.004109459],"category_scores_gemma":[0.00003724728,0.00007888886,0.00006210761,0.000218172,0.00005291787,0.00006393277,0.00002730857,0.0003552965,0.0005623514],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000904578,"about_ca_system_score_gemma":0.00002501732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004274253,"about_ca_topic_score_gemma":4.530534e-7,"domain_scores_codex":[0.9993522,0.000002675735,0.000127217,0.0001635671,0.0001774782,0.0001768412],"domain_scores_gemma":[0.9994468,0.00002713165,0.00004066433,0.0003365784,0.0000434262,0.0001053842],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0007758511,0.0008388576,0.7277074,0.0003267239,0.000203373,0.0001212318,0.0008135082,0.000006472821,0.02074292,0.00411844,0.1514054,0.09293975],"study_design_scores_gemma":[0.000623427,0.0003132027,0.1931969,0.00008854094,0.00003483024,0.00006127328,0.00004984586,0.0002679154,0.04933383,0.00002535536,0.7558226,0.0001823],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.799552,0.0000451727,0.00001362304,0.000890632,0.0006712176,0.00007075205,0.000003359485,0.00006244909,0.1986908],"genre_scores_gemma":[0.9621785,0.0000130171,0.001265008,0.0008649294,0.000670385,0.000004298905,0.00001107482,0.00001681078,0.03497599],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6044171,"threshold_uncertainty_score":0.9968009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007599886502377117,"score_gpt":0.3216605099920345,"score_spread":0.3140606234896574,"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."}}