{"id":"W4318811885","doi":"10.1109/icekim55072.2022.00206","title":"Research on Human Resource Management System at Black Saber Software Company","year":2022,"lang":"en","type":"article","venue":"2022 3rd International Conference on Education, Knowledge and Information Management (ICEKIM)","topic":"Gender Diversity and Inequality","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; University of Toronto","funders":"","keywords":"Black box; Software; Human resource management; Computer science; Promotion (chess); Process (computing); Poisson regression; Human resource management system; Human resources; Focus (optics); Knowledge management; Artificial intelligence; Economics; Management","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004122794,0.0002052584,0.000271045,0.002914145,0.001943314,0.001678827,0.0005280453,0.0002803641,0.005560401],"category_scores_gemma":[0.01213352,0.0001606009,0.0002149277,0.003841119,0.0007310602,0.001209305,0.001163232,0.0009146642,0.0008753759],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003290013,"about_ca_system_score_gemma":0.004008193,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03304606,"about_ca_topic_score_gemma":0.04248235,"domain_scores_codex":[0.9954221,0.001192896,0.0002369134,0.0007074189,0.001706505,0.0007341101],"domain_scores_gemma":[0.9889248,0.003847512,0.002229264,0.000565022,0.00304145,0.001392024],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007898342,0.0002205882,0.8737118,0.0001715081,0.00003395149,0.0001897936,0.01800991,0.0005044673,0.0006463697,0.003257122,0.006588398,0.09658716],"study_design_scores_gemma":[0.000003011086,0.0001180436,0.9703453,0.0001052113,0.00001124444,0.00005995371,0.01704083,0.0009255726,0.000413696,0.0005411637,0.01041783,0.0000179713],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9853436,0.0003005837,0.001315815,0.001007764,0.00002654085,0.00005690919,0.001088995,0.00002674442,0.01083305],"genre_scores_gemma":[0.9933376,0.0002417218,0.00120055,0.0001817682,0.00002133292,0.000121137,0.0007721956,0.00001580327,0.004107772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03304606,"threshold_uncertainty_score":0.06570745,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1752186146999875,"score_gpt":0.3962696436032401,"score_spread":0.2210510289032526,"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."}}