{"id":"W7130967733","doi":"10.5281/zenodo.18731868","title":"ICT Infrastructure Development and Economic Growth in Ethiopia,","year":2001,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Economic Growth and Development","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Information and Communications Technology; Investment (military); Panel data; Developing country; Regression analysis; Information technology; Digital divide","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"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":[0.0003811433,0.0002321298,0.0001086426,0.0009635474,0.0002826012,0.0009892045,0.0001186332,0.0001796958,0.001950082],"category_scores_gemma":[0.001096688,0.0001141728,0.0001294979,0.00130758,0.0002638187,0.0007265304,0.0004662246,0.0004049037,0.0002333522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009331417,"about_ca_system_score_gemma":0.0009569529,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01004574,"about_ca_topic_score_gemma":0.01294756,"domain_scores_codex":[0.9998635,0.0000384542,0.000005859952,0.00001117849,0.00001565763,0.0000653444],"domain_scores_gemma":[0.9994634,0.0001921889,0.0001571772,0.000009869777,0.00008902526,0.00008842146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003137747,0.000248056,0.9245448,0.0001905117,0.0001217177,0.001487782,0.001106549,0.009543227,0.0009711156,0.01450513,0.00531765,0.04164984],"study_design_scores_gemma":[0.00002471003,0.0001493119,0.9685939,0.0001595811,0.00007690112,0.0006498754,0.00475735,0.006335072,0.001130373,0.003284306,0.01482093,0.00001766541],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.985552,0.001709873,0.0002767883,0.0008483753,0.0000229434,0.00001109249,0.0007406213,0.000008423101,0.01082998],"genre_scores_gemma":[0.9972368,0.001194621,0.0001267028,0.00003980649,0.000007652472,0.000005807315,0.000219339,0.000001375478,0.001167927],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01004574,"threshold_uncertainty_score":0.01997453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01809674731740783,"score_gpt":0.2093417119525914,"score_spread":0.1912449646351836,"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."}}