{"id":"W1826901966","doi":"","title":"Optimiser communication et publicité pour les PME-PMI","year":2005,"lang":"fr","type":"article","venue":"Revue internationale P M E Économie et gestion de la petite et moyenne entreprise","topic":"Cultural Insights and Digital Impacts","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Trois-Rivières","funders":"","keywords":"Computer science","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0165781,0.0007794035,0.0005735751,0.002306948,0.003245544,0.01524458,0.00166217,0.004724161,0.05236254],"category_scores_gemma":[0.04538442,0.0004498829,0.0008283518,0.003904328,0.002072146,0.01063006,0.004224294,0.004221056,0.007581154],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.009933693,"about_ca_system_score_gemma":0.01130591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0176293,"about_ca_topic_score_gemma":0.01221964,"domain_scores_codex":[0.9898162,0.005037966,0.0002822489,0.0008452324,0.002603654,0.001414719],"domain_scores_gemma":[0.9724451,0.01359586,0.002200873,0.002730338,0.006317262,0.002710612],"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":[0.00111546,0.0009837464,0.01529164,0.0006827683,0.0001143344,0.000340069,0.005496645,0.01175665,0.002585107,0.5612726,0.07416749,0.3261935],"study_design_scores_gemma":[0.0004739362,0.0009060162,0.03777787,0.001285118,0.0002015199,0.0004999197,0.01973604,0.05904206,0.008878736,0.2493085,0.6216584,0.0002319791],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.1645743,0.007947613,0.08112853,0.195392,0.002504226,0.0006626109,0.001742428,0.00203684,0.5440114],"genre_scores_gemma":[0.87722,0.002233697,0.03416127,0.00299964,0.0007886423,0.0004412884,0.0007677444,0.0003397214,0.08104792],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05236254,"threshold_uncertainty_score":0.1751701,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0933248920829191,"score_gpt":0.300144423358727,"score_spread":0.2068195312758079,"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."}}