{"id":"W2133623058","doi":"10.14778/1687627.1687729","title":"Creating competitive products","year":2009,"lang":"en","type":"article","venue":"Proceedings of the VLDB Endowment","topic":"Data Management and Algorithms","field":"Computer Science","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Skyline; Dominance (genetics); Set (abstract data type); Computer science; Competitive advantage; Data mining; Business; Marketing","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":[0.003877573,0.001920113,0.001676574,0.004045139,0.00201917,0.003294736,0.002877159,0.001994766,0.01183333],"category_scores_gemma":[0.01363522,0.001080961,0.002530049,0.003677061,0.001212376,0.005559537,0.003661478,0.001747789,0.002879699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050089,"about_ca_system_score_gemma":0.001939795,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001222895,"about_ca_topic_score_gemma":0.002219713,"domain_scores_codex":[0.9958596,0.001113499,0.0002379756,0.0008635356,0.001622912,0.0003024651],"domain_scores_gemma":[0.9918058,0.003625368,0.0007190027,0.001792471,0.001593446,0.0004639732],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008076786,0.001487758,0.0138941,0.001796904,0.0004462297,0.001410111,0.001509216,0.1110785,0.02130512,0.1311422,0.03835648,0.6767656],"study_design_scores_gemma":[0.0004382381,0.001412269,0.003843758,0.0003456367,0.0005014093,0.003170656,0.001890169,0.5908582,0.03267386,0.2233596,0.1412832,0.0002230631],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.153638,0.001433546,0.7893676,0.001328504,0.0002757921,0.002353175,0.002539308,0.002137899,0.04692621],"genre_scores_gemma":[0.1849078,0.0005147007,0.8044261,0.0002707619,0.0000774771,0.0007952509,0.002989127,0.0003642963,0.005654551],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01183333,"threshold_uncertainty_score":0.03958648,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01145182703798596,"score_gpt":0.2180680133827352,"score_spread":0.2066161863447492,"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."}}