{"id":"W1883627988","doi":"10.1109/cca.1993.348324","title":"Putting artificial intelligence to work","year":2002,"lang":"en","type":"article","venue":"","topic":"Big Data and Business Intelligence","field":"Business, Management and Accounting","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Weyerhauser (Canada)","funders":"","keywords":"Key (lock); Process (computing); Work (physics); Computer science; Advisory committee; Engineering management; Artificial intelligence; Engineering ethics; Management science; Engineering; Management; Computer security","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":[0.02360232,0.00162941,0.00175953,0.004430981,0.006902164,0.02367943,0.003432668,0.0123899,0.0161734],"category_scores_gemma":[0.03306699,0.0008498034,0.001233087,0.002225048,0.05381295,0.03657898,0.01268469,0.02041209,0.006836175],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006461089,"about_ca_system_score_gemma":0.008453322,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002945198,"about_ca_topic_score_gemma":0.001866637,"domain_scores_codex":[0.9795242,0.01234801,0.000766067,0.002293205,0.003758322,0.001310203],"domain_scores_gemma":[0.972357,0.01685837,0.0007601158,0.004758458,0.002967176,0.002298768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00003019254,0.00008330165,0.0003863093,0.0003849095,0.00006891238,0.0001097361,0.002903124,0.001032124,0.0002216168,0.9015514,0.05013275,0.04309575],"study_design_scores_gemma":[0.0000145614,0.00002509936,0.0001117787,0.000422168,0.00001220514,0.00003912437,0.001127226,0.0003025036,0.0001578432,0.7284816,0.269282,0.00002389527],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.004033978,0.06658634,0.05518859,0.6117308,0.01452866,0.00009802361,0.0001595901,0.0007958588,0.2468783],"genre_scores_gemma":[0.3550641,0.1178119,0.1213874,0.2658898,0.03127689,0.001029106,0.000670514,0.002072483,0.1047978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02367943,"threshold_uncertainty_score":0.1248225,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1644080336274888,"score_gpt":0.2923767666824408,"score_spread":0.127968733054952,"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."}}