{"id":"W4321122153","doi":"10.4236/ti.2023.141002","title":"Factors Hindering the Adoption of the Customs Electronic Licensing System (CELS) by Clearing and Forwarding Agents in Zambia","year":2023,"lang":"en","type":"article","venue":"Technology and Investment","topic":"Digital Economy and Work Transformation","field":"Social Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Clearing; Business; The Internet; Revenue; Sample (material); Process (computing); Population; Computer science; Finance; World Wide Web; Environmental health; Medicine","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001496623,0.0001613706,0.0001871962,0.0005139594,0.00188275,0.001946765,0.0003849216,0.0004101502,0.002385768],"category_scores_gemma":[0.005223962,0.0002459541,0.000140221,0.0005570257,0.0008380537,0.00103025,0.0009774216,0.0007031467,0.0001610429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001944121,"about_ca_system_score_gemma":0.00363175,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.09960161,"about_ca_topic_score_gemma":0.1636983,"domain_scores_codex":[0.9985857,0.0003934385,0.0001284929,0.00009537178,0.0001938787,0.0006031811],"domain_scores_gemma":[0.9973724,0.0008608449,0.001020368,0.00008807908,0.0003179781,0.0003403917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008819883,0.0003034296,0.8572838,0.0003664999,0.00003218421,0.002381363,0.0972684,0.000319272,0.004562508,0.002324669,0.001005611,0.03406402],"study_design_scores_gemma":[0.000005870846,0.0001237517,0.8792068,0.0002712997,0.00003085511,0.0003528669,0.1103661,0.0005411516,0.0004829118,0.0001480959,0.008447245,0.00002301967],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987339,0.000105694,0.00004102784,0.000241406,0.000001615763,0.0000180807,0.00001101372,0.000001287387,0.0008458684],"genre_scores_gemma":[0.9992108,0.0002098687,0.0001322463,0.00005294049,0.000001228723,0.00001011131,0.00001075581,0.000001066302,0.0003709562],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09960161,"threshold_uncertainty_score":0.1980438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02399609732423204,"score_gpt":0.2503039523676933,"score_spread":0.2263078550434613,"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."}}