{"id":"W4387297413","doi":"10.2139/ssrn.4568024","title":"Interplay Between Amazon Store and Logistics","year":2023,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Digital Transformation in Industry","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Baycrest Hospital; University of Toronto","funders":"","keywords":"Amazon rainforest; Business; Computer science; Biology","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003836096,0.00009696739,0.0000997246,0.00012116,0.00005909755,0.00007300673,0.0001174994,0.00007753328,0.000009778504],"category_scores_gemma":[0.00002050025,0.00009368892,0.00003033412,0.0001799257,0.00002867837,0.0002289726,0.00001376205,0.001229278,0.00009715612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002913801,"about_ca_system_score_gemma":0.0001158162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001469739,"about_ca_topic_score_gemma":0.00001572328,"domain_scores_codex":[0.9986443,0.000009076572,0.0001838731,0.00006072933,0.0001255425,0.0009764752],"domain_scores_gemma":[0.9997843,0.00004313506,0.00002092221,0.00006537543,0.00001914283,0.00006713506],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002896274,0.00003119916,0.03995917,0.0002265276,0.001178854,0.00004314636,0.002018703,0.03586967,0.0004740586,0.2609674,0.01219875,0.6470036],"study_design_scores_gemma":[0.003469856,0.001078844,0.02425885,0.0003993906,0.0002358584,0.001988898,0.02308455,0.02135141,0.001604542,0.8309059,0.08949872,0.002123138],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8254623,0.001022456,0.1229831,0.0004564427,0.000896081,0.000158474,0.00004117307,0.0009760412,0.04800399],"genre_scores_gemma":[0.9980009,0.001143813,0.00002174786,0.000006708175,0.0002205753,0.000001896932,0.00001024614,0.00002434352,0.0005697492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6448804,"threshold_uncertainty_score":0.5340669,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01467253975269905,"score_gpt":0.2569141589906001,"score_spread":0.242241619237901,"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."}}