{"id":"W393363030","doi":"10.2480/agrmet.921","title":"Risk Assessment and Regionalization of Agro-meteorological Hazards in Jilin Province, China","year":2005,"lang":"en","type":"article","venue":"Journal of Agricultural Meteorology","topic":"Climate change impacts on agriculture","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Science North","funders":"","keywords":"China; Meteorological disasters; Agriculture; Environmental science; Temperate climate; Waterlogging (archaeology); Agricultural productivity; Natural hazard; Geography; Sustainable development; Distribution (mathematics); Global warming; Hazard; Climate change; Environmental protection; Meteorology; Ecology","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":[0.0009695289,0.0003534049,0.0002169487,0.001519138,0.0002796332,0.0006232029,0.0003404767,0.000216136,0.0004031077],"category_scores_gemma":[0.002222547,0.0001701686,0.0003023813,0.0008276457,0.0002452238,0.0003010896,0.0005972352,0.0001474484,0.00003144479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001500431,"about_ca_system_score_gemma":0.00111946,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05480678,"about_ca_topic_score_gemma":0.04965317,"domain_scores_codex":[0.9996575,0.0001293854,0.00002012756,0.00004407959,0.0000926441,0.0000562385],"domain_scores_gemma":[0.999161,0.0002627452,0.000223787,0.00004752161,0.0002351702,0.00006981909],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002353806,0.00006536545,0.7317967,0.00008301029,0.0002074841,0.0006889593,0.0004823123,0.2310389,0.002420143,0.002135604,0.0006568815,0.03018925],"study_design_scores_gemma":[0.00003664839,0.0001378867,0.5750342,0.00002607583,0.0001233742,0.0002086832,0.00101914,0.4185915,0.001042333,0.002292653,0.001460798,0.00002671098],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9945393,0.0001862356,0.004006791,0.0001322834,0.000003291616,0.00004989186,0.0001895677,0.00002209268,0.0008704686],"genre_scores_gemma":[0.9985794,0.0000884833,0.0009895324,0.00000415207,0.000001817148,0.00001248084,0.0001536884,0.000001311037,0.0001691361],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05480678,"threshold_uncertainty_score":0.1089755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01591711664738597,"score_gpt":0.2631864376496006,"score_spread":0.2472693210022146,"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."}}