{"id":"W2599820070","doi":"10.5430/ijba.v8n2p86","title":"Inverse Optimization Method of Resource Allocation with Consideration of Advantage Structure","year":2017,"lang":"en","type":"article","venue":"International Journal of Business Administration","topic":"Competency Development and Evaluation","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Computer science; Inverse; Mathematical optimization; Competence (human resources); Inverse problem; Operations research; Mathematics","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":[0.0005222963,0.0001156781,0.0002232061,0.000262374,0.00007938713,0.00007179866,0.0003233135,0.00008638885,0.0004511751],"category_scores_gemma":[0.0003140632,0.0001007171,0.00004843683,0.0000966417,0.00009096636,0.0006849563,0.00002469825,0.00009906097,0.000001227716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005322881,"about_ca_system_score_gemma":0.0003643011,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002296318,"about_ca_topic_score_gemma":0.00007865384,"domain_scores_codex":[0.9983228,0.0001054454,0.0007433427,0.0001352378,0.0006165332,0.00007660271],"domain_scores_gemma":[0.994418,0.00009555108,0.002534186,0.0002240339,0.0026947,0.0000334992],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.01790139,0.002120684,0.1523148,0.0003572003,0.003069675,0.000279729,0.01144055,0.4339421,0.1742464,0.0724331,0.003231164,0.1286631],"study_design_scores_gemma":[0.009466036,0.001385516,0.852667,0.0009472145,0.0004857475,0.001436445,0.003713541,0.02749062,0.09637249,0.003833959,0.00156572,0.0006356752],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4506914,0.00002811428,0.5426138,0.002275509,0.001279059,0.0002073995,0.0000234617,0.000007122555,0.002874096],"genre_scores_gemma":[0.9508051,0.00001270724,0.04874101,0.00003331785,0.0001917987,0.000002549194,0.00009771833,0.00001103646,0.0001047749],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7003523,"threshold_uncertainty_score":0.4940051,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03299432362415281,"score_gpt":0.3708711292479683,"score_spread":0.3378768056238154,"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."}}