{"id":"W4280600334","doi":"10.1016/j.eswa.2022.117523","title":"A rough set-based Competitive Intelligence approach for anticipating competitor’s action","year":2022,"lang":"en","type":"article","venue":"Expert Systems with Applications","topic":"Rough Sets and Fuzzy Logic","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Rimouski","funders":"","keywords":"Competitor analysis; Competition (biology); Computer science; Competitive intelligence; Knowledge management; Process (computing); Competitive advantage; Information technology; Product (mathematics); Set (abstract data type); Information system; Empirical research; Strategic management; Discipline; Business intelligence; Marketing; Business","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":[0.001666729,0.0006851386,0.001465679,0.00191213,0.000785218,0.002556034,0.001994719,0.001306771,0.002578205],"category_scores_gemma":[0.004611666,0.0003769844,0.001349197,0.001567706,0.0008995095,0.002803227,0.001134556,0.001166266,0.0003856445],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169738,"about_ca_system_score_gemma":0.001745626,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007986363,"about_ca_topic_score_gemma":0.005797783,"domain_scores_codex":[0.9987901,0.0002932523,0.00007549154,0.0002011083,0.0005248379,0.0001152427],"domain_scores_gemma":[0.9986278,0.0007275397,0.0001663485,0.00006863852,0.0003310893,0.00007870136],"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":[0.0001727067,0.0002254039,0.001877145,0.0002492152,0.0002982946,0.000452108,0.0005489599,0.709939,0.003354451,0.1551401,0.002830866,0.1249117],"study_design_scores_gemma":[0.000007167213,0.00006009745,0.0002990045,0.00001154384,0.00003277966,0.00003721769,0.00005197357,0.9777181,0.0003302258,0.02074708,0.0006808573,0.00002404326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02032551,0.0005505832,0.970045,0.0004649768,0.0001300819,0.00008719657,0.00007642895,0.0001552437,0.008164926],"genre_scores_gemma":[0.7416325,0.00064422,0.2535212,0.0001709907,0.0001358794,0.0001462248,0.000115497,0.00002903576,0.003604491],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007986363,"threshold_uncertainty_score":0.01587975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0655482291394944,"score_gpt":0.3110561721831803,"score_spread":0.2455079430436859,"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."}}