{"id":"W1980341927","doi":"10.5539/ass.v10n5p55","title":"Anticipation of Penalty Kick to a Goal Keeper","year":2014,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Sports Performance and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Anticipation (artificial intelligence); Correlation; Correlation coefficient; Linear regression; Confidence interval; Statistics; Positive correlation; Linear relationship; Linear correlation; Regression analysis; Pearson product-moment correlation coefficient; Negative correlation; Psychology; Mathematics; Computer science; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001204048,0.0003117705,0.000215061,0.0005736811,0.0003934474,0.0009387293,0.0003803819,0.0005039169,0.008381943],"category_scores_gemma":[0.01404377,0.0001918086,0.0004420613,0.0002461188,0.0002891876,0.0004068269,0.0006238388,0.001204927,0.001138026],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005201247,"about_ca_system_score_gemma":0.0005873179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001877181,"about_ca_topic_score_gemma":0.002975798,"domain_scores_codex":[0.9989411,0.0003426932,0.00008165459,0.0001038926,0.0003695382,0.0001611218],"domain_scores_gemma":[0.9853843,0.004600645,0.006806271,0.0004406101,0.001411116,0.001357052],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0006022385,0.0006111466,0.9612313,0.0001680439,0.000131326,0.0003626047,0.003712804,0.001436407,0.002917709,0.0005048761,0.001060691,0.02726084],"study_design_scores_gemma":[0.00000855901,0.0008267655,0.9891339,0.00004443726,0.00002980672,0.0001597717,0.006152146,0.001216261,0.0006712741,0.0002675694,0.001457761,0.00003179297],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950027,0.00003929706,0.0007254004,0.0001503156,0.00001654819,0.00002513204,0.00006904094,0.00002229457,0.003949186],"genre_scores_gemma":[0.9971877,0.00007274014,0.0006587974,0.0000509729,0.000009628297,0.00003011695,0.0001141091,0.000005063909,0.001870849],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008381943,"threshold_uncertainty_score":0.02804035,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01884787423185725,"score_gpt":0.329526390508141,"score_spread":0.3106785162762838,"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."}}