{"id":"W1588191683","doi":"10.3968/4252","title":"Designing Template for Talent Identification and Development in Sport","year":2014,"lang":"en","type":"article","venue":"Higher education of social science","topic":"Sports Performance and Training","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Competitor analysis; Identification (biology); Talent development; Business; Talent management; Computer science; Knowledge management; Marketing; Psychology; Public relations; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005262762,0.00003332803,0.00007141336,0.000101117,0.0001111129,0.000009922873,0.00003581873,0.00001841211,0.00001400102],"category_scores_gemma":[0.000006572067,0.00003204442,0.000007483838,0.0001821469,0.0001078032,0.0001203941,0.00000639084,0.00002332559,0.000001722625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004866051,"about_ca_system_score_gemma":0.0003083487,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007932307,"about_ca_topic_score_gemma":4.487709e-7,"domain_scores_codex":[0.9994964,8.477795e-7,0.0001570366,0.0001167889,0.0001365587,0.00009237543],"domain_scores_gemma":[0.9997565,0.000004429755,0.00007767633,0.00004022121,0.00008908655,0.00003215778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002974341,0.0001558373,0.8935281,0.0001178354,0.000003443478,1.012228e-7,0.008745844,0.000001269511,0.02424369,0.03019041,0.0001157138,0.042868],"study_design_scores_gemma":[0.0001796238,0.00001497383,0.9728496,0.00003267104,0.000003755811,7.743579e-7,0.0003437913,0.000008218005,0.01198052,0.00008444969,0.01446661,0.00003499578],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957308,0.00001707015,0.000190622,0.0001490102,0.00020209,0.0001572504,8.429135e-8,0.000006840098,0.003546188],"genre_scores_gemma":[0.9947401,0.00000178365,0.004233931,0.00009156016,0.00007483319,0.00003284454,0.00000534569,0.000002728093,0.0008169174],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0793215,"threshold_uncertainty_score":0.1306733,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03689290803851957,"score_gpt":0.3423049427639406,"score_spread":0.305412034725421,"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."}}