{"id":"W7144942360","doi":"","title":"ミャンマー豆農家の気候効果を配慮した生産効率分析と気象インデックスに基づく作物損害保険に対する実現可能性の検討","year":2018,"lang":"en","type":"dissertation","venue":"Institutional Repositories DataBase (IRDB)","topic":"Military Technology and Strategies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Education, Culture, Sports, Science and Technology","keywords":"Process (computing); Identification (biology); Product (mathematics)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001418365,0.0003653163,0.0003819506,0.003747083,0.001232178,0.003541674,0.0004731258,0.0004310275,0.1192006],"category_scores_gemma":[0.004514033,0.0001819121,0.0002854551,0.00837606,0.0004311364,0.002706187,0.001076448,0.0008342956,0.07360882],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001407859,"about_ca_system_score_gemma":0.004347027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005476838,"about_ca_topic_score_gemma":0.009678115,"domain_scores_codex":[0.9995683,0.0000507005,0.00005335973,0.00005485545,0.000216802,0.00005607716],"domain_scores_gemma":[0.9976577,0.0004165913,0.0002510475,0.0002875278,0.001096313,0.0002908554],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"observational","study_design_scores_codex":[0.00009743952,0.0001501343,0.009419372,0.001112772,0.00001629027,0.000101997,0.001368048,0.0002702346,0.00203798,0.01705035,0.6388108,0.3295646],"study_design_scores_gemma":[0.000007675092,0.00002178267,0.01606761,0.0003629428,0.0000134137,0.00008184618,0.001217286,0.0001173341,0.001520604,0.002798372,0.9777787,0.00001242063],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.03024662,0.01081642,0.004206719,0.01073711,0.001166344,0.0003723648,0.2128149,0.001606345,0.7280332],"genre_scores_gemma":[0.09959414,0.03888143,0.01443099,0.0009223876,0.0007805052,0.000650622,0.2466362,0.0007140256,0.5973896],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1192006,"threshold_uncertainty_score":0.3987659,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009194237160705014,"score_gpt":0.2356285376150524,"score_spread":0.2264343004543474,"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."}}