{"id":"W2290169688","doi":"10.5539/mas.v10n5p21","title":"Investigation and Prioritizing the Effective Factors on Increasing the Human Resources Productivity in Agriculture Bank Using Multi-Attribute Decision Making Model","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Efficiency Analysis Using DEA","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Productivity; TOPSIS; Ranking (information retrieval); Human resources; Computer science; Agriculture; Statistical population; Descriptive statistics; Population; Operations research; Environmental economics; Knowledge management; Operations management; Statistics; Economics; Mathematics; Management; 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":[0.004983332,0.0009938926,0.0008043549,0.003310625,0.0009261805,0.004071696,0.0008011421,0.000902169,0.002986251],"category_scores_gemma":[0.005169399,0.0004219313,0.001316477,0.002583827,0.0005863301,0.001812468,0.001424857,0.0009641305,0.0001487037],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002714041,"about_ca_system_score_gemma":0.003865975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004785047,"about_ca_topic_score_gemma":0.00525451,"domain_scores_codex":[0.9969094,0.001578947,0.000186733,0.000209482,0.0007157124,0.0003998425],"domain_scores_gemma":[0.9957509,0.003181014,0.0002686072,0.00004815558,0.0005508522,0.0002004398],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007822801,0.001909499,0.1019699,0.002008932,0.0006961698,0.001089201,0.003077789,0.687531,0.007101345,0.0192502,0.002590095,0.1719936],"study_design_scores_gemma":[0.00006604988,0.0006619735,0.02339803,0.0003140593,0.0002360756,0.0000975869,0.004858972,0.9564852,0.00259929,0.008830383,0.002368478,0.00008394782],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8597669,0.0009149417,0.1236565,0.0009506658,0.00005939872,0.00123088,0.0003265621,0.00009222807,0.01300185],"genre_scores_gemma":[0.9613676,0.0005941762,0.03648445,0.00004087312,0.00001389201,0.0003517351,0.0001204541,0.000006924664,0.001019876],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004983332,"threshold_uncertainty_score":0.02635473,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09417223909477854,"score_gpt":0.3539234900680829,"score_spread":0.2597512509733044,"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."}}