{"id":"W264298798","doi":"","title":"An empirical investigation of intelligent agents for e-business customer relationship management: a knowledge management perspective.","year":2003,"lang":"en","type":"article","venue":"European Conference on Information Systems","topic":"Multi-Agent Systems and Negotiation","field":"Computer Science","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Knowledge management; Perspective (graphical); Computer science; Intelligent agent; Personal knowledge management; Customer relationship management; Test (biology); Customer knowledge; Business; Customer advocacy; Organizational learning; Artificial intelligence; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.01027645,0.0002539866,0.0003159543,0.00279996,0.002765064,0.004982679,0.001483768,0.002602127,0.004292865],"category_scores_gemma":[0.04716619,0.0004158605,0.0003154258,0.0035077,0.002328012,0.007248753,0.002265504,0.002066894,0.000458086],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003261124,"about_ca_system_score_gemma":0.002269126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004236072,"about_ca_topic_score_gemma":0.007342114,"domain_scores_codex":[0.9923624,0.004949117,0.000381071,0.0003265039,0.001560173,0.0004208128],"domain_scores_gemma":[0.9094204,0.07350773,0.009652279,0.001208938,0.004518603,0.001692033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003596357,0.003627681,0.5638363,0.001434212,0.0002570235,0.002085303,0.1379844,0.001846227,0.0004624182,0.1218102,0.01159594,0.1547006],"study_design_scores_gemma":[0.0001520409,0.0004449264,0.5036004,0.001687003,0.0002652819,0.001433026,0.3755153,0.02464011,0.0009605388,0.03115519,0.06003335,0.0001127879],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9391647,0.004308042,0.002435567,0.008677292,0.00004738602,0.0003314673,0.0001439949,0.00001440967,0.0448771],"genre_scores_gemma":[0.9966173,0.0008543436,0.001192699,0.0003025538,0.000008910282,0.0001025378,0.00005461958,0.000004088975,0.0008630041],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01027645,"threshold_uncertainty_score":0.05434769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1071413721593419,"score_gpt":0.3273184049894441,"score_spread":0.2201770328301022,"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."}}