{"id":"W1979064470","doi":"10.3182/20060517-3-fr-2903.00239","title":"OPTIMAL PRODUCT SEGMENTATION WITH STRATEGIC CUSTOMERS","year":2006,"lang":"en","type":"article","venue":"IFAC Proceedings Volumes","topic":"Supply Chain and Inventory Management","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Ticket; Market segmentation; Business; Product (mathematics); Function (biology); Limit (mathematics); Marketing; Yield (engineering); Segmentation; Computer science; Computer security; Mathematics; Artificial intelligence","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":[0.001394347,0.001199436,0.00295811,0.001334734,0.0009614877,0.002675403,0.001307711,0.002513044,0.01084187],"category_scores_gemma":[0.004769832,0.001743434,0.0009203663,0.002004061,0.001847705,0.003161316,0.00163451,0.001155933,0.001183351],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002222791,"about_ca_system_score_gemma":0.00224568,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003100202,"about_ca_topic_score_gemma":0.003119814,"domain_scores_codex":[0.9990583,0.0003231655,0.00002693985,0.0002290285,0.0001050994,0.0002575262],"domain_scores_gemma":[0.997811,0.00140008,0.0001571976,0.0001766852,0.0002111,0.0002438709],"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":[0.002508681,0.0006883839,0.001842349,0.0004222944,0.0001183694,0.000379665,0.0003449662,0.6989572,0.007420572,0.1654853,0.009990608,0.1118417],"study_design_scores_gemma":[0.0001579748,0.0002405103,0.0007047906,0.00002971902,0.000049783,0.00006732474,0.0001454228,0.8715155,0.001637766,0.123959,0.001463207,0.00002909594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3860093,0.001057686,0.5383585,0.002399377,0.0002186056,0.000279695,0.0003888109,0.000842033,0.07044601],"genre_scores_gemma":[0.941134,0.0002405673,0.04767109,0.0001898689,0.00008851477,0.00007005822,0.00015147,0.00008846683,0.01036589],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01084187,"threshold_uncertainty_score":0.03626966,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01398915227012895,"score_gpt":0.2043005037167485,"score_spread":0.1903113514466196,"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."}}