{"id":"W3110254642","doi":"10.18280/ria.340515","title":"An Evaluation Strategy for Commercial Precision Marketing Based on Artificial Neural Network","year":2020,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Digital Marketing and Social Media","field":"Social Sciences","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial neural network; Principal component analysis; Computer science; MATLAB; The Internet; Cluster analysis; Principal (computer security); Data mining; Index (typography); Marketing research; Value (mathematics); Artificial intelligence; Machine learning; Marketing; Business; World Wide Web","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005640523,0.0001741637,0.0002361062,0.00004685024,0.0009653984,0.0003275596,0.0004017383,0.0001568559,0.0003642786],"category_scores_gemma":[0.005651741,0.0001894201,0.0001503868,0.0005430735,0.0001764885,0.0002509491,0.00002200093,0.0002065225,0.00007847868],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009523326,"about_ca_system_score_gemma":0.0002046024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006586673,"about_ca_topic_score_gemma":0.0002368638,"domain_scores_codex":[0.9967408,0.001121238,0.0004795375,0.0004955818,0.0006078091,0.0005550796],"domain_scores_gemma":[0.996964,0.002033494,0.0001726098,0.0002301164,0.0002858788,0.0003138742],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005439192,0.0001236597,0.0003788617,0.00002020585,0.000004216936,0.000001451715,0.003411348,0.3659226,0.00009018705,0.003227187,0.001542794,0.6247336],"study_design_scores_gemma":[0.00006854428,0.0004528293,0.0003861665,0.00007585975,0.0000249259,9.895282e-8,0.004959635,0.9822675,0.0003731956,0.002893298,0.00824933,0.0002486372],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.726871,0.0002139914,0.08930771,0.02217319,0.004518087,0.005549464,0.00007819542,0.0007942544,0.1504942],"genre_scores_gemma":[0.9956779,0.000007809338,0.0005460394,0.00084094,0.002667982,0.00008753471,0.0000450423,0.00002688321,0.00009990363],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6244849,"threshold_uncertainty_score":0.7724328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1478113581758519,"score_gpt":0.3761885446972185,"score_spread":0.2283771865213666,"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."}}