{"id":"W7005314746","doi":"","title":"Portrait de l'intelligence compétitive dans les petites et moyennes entreprises québécoises : comment faire de l'intelligence intelligente?","year":2015,"lang":"fr","type":"other","venue":"Knowledge UdeS (Institutional Deposit of the University of Sherbrooke)","topic":"Crustacean biology and ecology","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Nested logit; Choice of law; Context (archaeology)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001007743,0.0004046217,0.0002889189,0.001491006,0.006251785,0.006567963,0.00105456,0.001704667,0.01219263],"category_scores_gemma":[0.001774446,0.000196414,0.0003806725,0.002094923,0.005132645,0.003411253,0.001525245,0.001847812,0.0008113458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02045741,"about_ca_system_score_gemma":0.01336997,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7748043,"about_ca_topic_score_gemma":0.8484871,"domain_scores_codex":[0.9993163,0.000175172,0.00001193301,0.0001096423,0.0002118017,0.000175132],"domain_scores_gemma":[0.9987921,0.0002104246,0.000118035,0.00005986922,0.0004837285,0.0003359081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001210342,0.00009014548,0.04221655,0.0002257047,0.00007174711,0.001657738,0.1046748,0.006470053,0.001836715,0.7504292,0.02608393,0.06612232],"study_design_scores_gemma":[0.00003582468,0.000116165,0.07860949,0.0006467766,0.0001081718,0.0006483851,0.1865852,0.02388582,0.001244976,0.1118658,0.5960395,0.0002138252],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.4047633,0.003668587,0.01650421,0.03658418,0.0002053554,0.00008760978,0.0007059231,0.0001054124,0.5373755],"genre_scores_gemma":[0.9305671,0.001680959,0.003848423,0.000930071,0.00002315529,0.00003738966,0.000195759,0.00003134543,0.0626858],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2251957,"threshold_uncertainty_score":0.4530438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01646484967914479,"score_gpt":0.2217091927471237,"score_spread":0.205244343067979,"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."}}