{"id":"W2600362226","doi":"","title":"STREAMLINING HUMAN RESOURCE MANAGEMENT AT ENTERPRISES OPERATING WITHIN KAZAKHSTANÃ¢ÂÂS PRESENT-DAY AGRO-INDUSTRIAL COMPLEX","year":2016,"lang":"en","type":"article","venue":"The Journal of Internet Banking and Commerce","topic":"Digitalization and Economic Development in Agriculture","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agrarian society; Human resources; Human resource management; Agriculture; Business; Human capital; Work (physics); Population; Industrial organization; Economic growth; Knowledge management; Computer science; Economics; Management; Engineering; Geography","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":[0.001023685,0.0001914404,0.0002411123,0.0001282202,0.0002670226,0.0004538575,0.0004890672,0.00004898949,0.0004339661],"category_scores_gemma":[0.00005281753,0.000100354,0.00006961888,0.0001034519,0.00007166329,0.0006143161,0.0005122247,0.0001475653,0.00003378597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006976035,"about_ca_system_score_gemma":0.000005688327,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003205876,"about_ca_topic_score_gemma":0.00001805441,"domain_scores_codex":[0.9987152,0.00005405033,0.0006410468,0.0001479054,0.0002275901,0.0002142188],"domain_scores_gemma":[0.9987529,0.000123501,0.0008677217,0.0001478756,0.00008382661,0.00002419582],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004772874,0.0002868243,0.1359655,0.000185976,0.001108485,0.00007225613,0.004982535,0.0006412421,0.002997862,0.03590832,0.7303278,0.08704591],"study_design_scores_gemma":[0.005978722,0.0001428928,0.02848215,0.003802948,0.0003640742,0.0002169736,0.009939598,0.001137401,0.0008081499,0.002122739,0.9460216,0.000982763],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9120978,0.00006772921,0.000901633,0.003036777,0.0004440201,0.0001698343,0.000002326019,0.00004513545,0.0832348],"genre_scores_gemma":[0.989509,0.00001876644,0.00006059552,0.00135693,0.000795773,0.000001890644,0.000009501966,0.00001867983,0.008228828],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2156938,"threshold_uncertainty_score":0.4751625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03957221846846443,"score_gpt":0.2330825945260131,"score_spread":0.1935103760575487,"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."}}