{"id":"W4394358136","doi":"10.6084/m9.figshare.14616818.v1","title":"Land use and land cover play weak roles in typhoon economic losses at the county level","year":2021,"lang":"en","type":"dataset","venue":"Figshare","topic":"Climate Change and Sustainable Development","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto","funders":"","keywords":"Typhoon; Cover (algebra); Land cover; Environmental science; Geography; Land use; Natural resource economics; Physical geography; Environmental resource management; Meteorology; Economics; Civil engineering; Engineering","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":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.00007091406,0.0002109647,0.0002088393,0.00002929272,0.0001208411,0.0001357208,0.0002151358,0.0001625874,0.3320051],"category_scores_gemma":[0.0001456748,0.0001573391,0.00003003315,0.00005951522,0.00002266292,0.0001535843,0.0008918772,0.0001785195,0.004192522],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005596233,"about_ca_system_score_gemma":0.00005575227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00182646,"about_ca_topic_score_gemma":0.06059591,"domain_scores_codex":[0.9989996,0.00003636669,0.0001669978,0.0003703427,0.0001534708,0.0002731974],"domain_scores_gemma":[0.9993539,0.0001836074,0.0000855867,0.0003105055,0.000006947304,0.00005940424],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00000991563,0.00001279096,0.007291443,0.00009259044,0.000008764175,0.00009701539,0.00004962597,0.0001292565,6.600256e-7,2.042129e-8,0.9922357,0.00007219451],"study_design_scores_gemma":[0.0001965147,0.000007154395,0.06103058,0.0003079834,0.000007165158,0.00001837577,0.00005652164,0.00001776674,0.00001251552,0.000002192071,0.9381304,0.0002128254],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.009693196,0.0005845256,4.879579e-9,0.0000722667,0.00004503091,0.0002104752,0.9891949,0.00000536386,0.0001941781],"genre_scores_gemma":[0.0001221396,0.0007359676,0.000001715042,0.0003177261,0.00004547082,0.00009285543,0.9969633,0.00001269356,0.001708124],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3278126,"threshold_uncertainty_score":0.9965828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05153671251354305,"score_gpt":0.2429819418641445,"score_spread":0.1914452293506015,"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."}}