{"id":"W2275602845","doi":"10.5539/mas.v10n4p47","title":"Product’s Selection for the Moroccan Technical Textile Industry by Using Custom’s Imports Data and Analytic Models","year":2016,"lang":"en","type":"article","venue":"Modern Applied Science","topic":"Global Trade and Competitiveness","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Analytic hierarchy process; Product (mathematics); Selection (genetic algorithm); Textile; Attractiveness; Investment (military); Value (mathematics); Manufacturing engineering; Textile industry; Business; Computer science; Industrial organization; Marketing; Operations research; Mathematics; Engineering; Statistics; Geography; Artificial intelligence","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":[0.0006745651,0.0001250905,0.0001134565,0.00007859555,0.0004959753,0.0002821577,0.000697966,0.00005373394,0.000009643478],"category_scores_gemma":[0.00003834312,0.00007524349,0.00001479109,0.0005313994,0.0003422988,0.001228842,0.0004586554,0.0001057652,0.000002503712],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000485782,"about_ca_system_score_gemma":0.00007938097,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001089482,"about_ca_topic_score_gemma":0.00002924084,"domain_scores_codex":[0.9986024,0.000002235226,0.0001485938,0.0006188583,0.0003009025,0.0003269901],"domain_scores_gemma":[0.9993384,0.00003009449,0.0001015097,0.000435835,0.0000736131,0.00002051188],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007189459,0.0001295406,0.01021489,0.00007225417,0.00001992099,9.280092e-7,0.00002827887,0.002351334,0.8868542,0.03406725,0.002519489,0.06367],"study_design_scores_gemma":[0.0002643361,0.000003691328,0.001587298,0.00002592432,0.0000518498,0.000007140024,0.00005257349,0.9765928,0.001203262,0.01421,0.005792008,0.0002090917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4280316,0.0001624021,0.562483,0.002467418,0.0002056469,0.001248128,0.00004867903,0.0001968301,0.005156304],"genre_scores_gemma":[0.9990613,0.000004246884,0.000271421,0.0003419122,0.0002124901,0.00003297073,0.00000430607,0.00001119441,0.00006012026],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9742415,"threshold_uncertainty_score":0.3814691,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05611084403253251,"score_gpt":0.2679366451817999,"score_spread":0.2118258011492674,"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."}}