{"id":"W2107770177","doi":"10.1109/hicss.2011.494","title":"When Competitive Intelligence Meets Geospatial Intelligence","year":2011,"lang":"en","type":"article","venue":"","topic":"Competitive and Knowledge Intelligence","field":"Business, Management and Accounting","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Geospatial analysis; Competitive intelligence; Business intelligence; Computer science; Competitive advantage; Data science; Knowledge management; Key (lock); Process (computing); Business; Computer security; Marketing; Geography","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":[0.007820253,0.0008565669,0.001005439,0.006732061,0.005126943,0.02100247,0.001220535,0.005287643,0.01278297],"category_scores_gemma":[0.0311392,0.0003804443,0.000703681,0.005703551,0.0145545,0.02434002,0.008803679,0.002857128,0.002266032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006509013,"about_ca_system_score_gemma":0.006748207,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007374372,"about_ca_topic_score_gemma":0.007607079,"domain_scores_codex":[0.9846619,0.003283148,0.0007982666,0.001711903,0.006607933,0.002936781],"domain_scores_gemma":[0.9833645,0.006219293,0.003127667,0.001155761,0.004205931,0.001926865],"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":[0.00004255709,0.0000581208,0.005647344,0.00009875504,0.00002496026,0.0004827173,0.00167342,0.0007000708,0.0001999024,0.9718758,0.003328371,0.01586786],"study_design_scores_gemma":[0.00003725876,0.0001001347,0.007810597,0.0002706366,0.00003554499,0.0008216759,0.01553343,0.005112675,0.0004335128,0.9013745,0.06838848,0.00008159843],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.06145439,0.001470864,0.02254128,0.02005658,0.0004658013,0.0002171325,0.000207372,0.00007753869,0.8935091],"genre_scores_gemma":[0.9821269,0.0008342458,0.008159256,0.001767022,0.0003849122,0.0001621157,0.0001163751,0.00003018148,0.006418934],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02100247,"threshold_uncertainty_score":0.04722649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05526523439538599,"score_gpt":0.2440078479139822,"score_spread":0.1887426135185962,"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."}}