{"id":"W6965449748","doi":"10.34989/swp-2023-8","title":"Climate Variability and International Trade","year":2023,"lang":"en","type":"article","venue":"Econstor (Econstor)","topic":"Forest Ecology and Biodiversity Studies","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Storm; Storm surge; Identification (biology); Geocoding; Economic impact analysis; Matching (statistics)","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":[0.0003666978,0.0002325582,0.0002285045,0.0009236034,0.0003259466,0.001704262,0.0001662836,0.0003741436,0.003392128],"category_scores_gemma":[0.001774432,0.00008295265,0.0003590279,0.002695915,0.0004167133,0.0007308143,0.0006360454,0.0004743804,0.0003620367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006888371,"about_ca_system_score_gemma":0.0002656466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00654697,"about_ca_topic_score_gemma":0.005718043,"domain_scores_codex":[0.9998005,0.00005565103,0.00001148373,0.00004792348,0.00003945839,0.00004505439],"domain_scores_gemma":[0.9987816,0.0003901196,0.0005480949,0.00007902586,0.0001061712,0.00009499504],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001267094,0.00008007324,0.8285184,0.000118408,0.0004588326,0.0004118672,0.0005859583,0.06030973,0.0007619653,0.04416918,0.008036783,0.05642207],"study_design_scores_gemma":[0.00001316398,0.00006193746,0.8971821,0.0001171191,0.00007524218,0.0002943921,0.001065561,0.02768692,0.0003162351,0.03505572,0.03808728,0.00004432393],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8853559,0.009569446,0.004608107,0.002874039,0.0002054611,0.00001665631,0.003602626,0.0001097697,0.09365792],"genre_scores_gemma":[0.9966905,0.001357633,0.0001662747,0.00009652592,0.0000991878,0.00000487622,0.0005715414,0.00001657732,0.0009968296],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00654697,"threshold_uncertainty_score":0.01301771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02122060431662642,"score_gpt":0.2196944092165228,"score_spread":0.1984738048998963,"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."}}