{"id":"W4313247499","doi":"10.31217/p.36.2.2","title":"Cross examinations of maritime trade disruptions in Africa during COVID-19 pandemic","year":2022,"lang":"en","type":"article","venue":"Pomorstvo","topic":"Maritime Ports and Logistics","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pandemic; Quarter (Canadian coin); Coronavirus disease 2019 (COVID-19); Geography; Supply chain; Business; Container (type theory); International trade; World trade; Engineering; Marketing; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0001928043,0.00009272464,0.0001414795,0.0001597552,0.0001657711,0.00001564379,0.0001423518,0.00003533837,0.002037202],"category_scores_gemma":[0.00009410523,0.0001116665,0.00004490897,0.0002846929,0.00005661231,0.00005314732,0.00007552638,0.0002221021,0.000003175448],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002249879,"about_ca_system_score_gemma":0.00002633995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001696288,"about_ca_topic_score_gemma":0.0001121513,"domain_scores_codex":[0.9991717,0.0000332428,0.0002745624,0.0001294204,0.0001552356,0.0002357795],"domain_scores_gemma":[0.9995952,0.000102136,0.00003066796,0.000175441,0.000006612051,0.00008993539],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.00002154535,0.0003903457,0.3337862,0.0006924257,0.00005591433,0.000335459,0.006774859,0.6410487,0.00367637,0.009306876,0.001837483,0.002073776],"study_design_scores_gemma":[0.00181078,0.00007690144,0.834344,0.0000231206,0.00004819633,0.0003351023,0.001078576,0.06449834,0.0003302358,0.002891568,0.09382831,0.0007348206],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9642265,0.0007136217,0.005243292,0.0002874844,0.0003843839,0.0003665032,0.0008062564,0.0004570019,0.02751499],"genre_scores_gemma":[0.9987959,0.00003701909,0.0001731671,0.00002221473,0.00002789809,0.00008110022,0.00004802447,0.00002080309,0.0007938628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5765504,"threshold_uncertainty_score":0.9988751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04160763231325395,"score_gpt":0.2772438105835331,"score_spread":0.2356361782702791,"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."}}