{"id":"W4306771028","doi":"10.1007/s10584-022-03444-6","title":"A canary, a coal mine, and imperfect data: determining the efficacy of open-source climate change models in detecting and predicting extreme weather events in Northern and Western Kenya","year":2022,"lang":"en","type":"article","venue":"Climatic Change","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"International Development Research Centre","keywords":"Extreme weather; Warning system; Climate change; Flood myth; Environmental science; Climatology; Predictive modelling; Early warning system; Agriculture; Meteorology; Geography; Computer science; Ecology; Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001251516,0.0001413445,0.000288912,0.00007568364,0.0002392315,0.00002108606,0.0003665624,0.00003986212,0.00003199012],"category_scores_gemma":[0.00005375711,0.0001161643,0.00001631304,0.0002312882,0.0001120401,0.000430904,0.002685629,0.0001811655,9.283892e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005696919,"about_ca_system_score_gemma":0.000004174366,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007424202,"about_ca_topic_score_gemma":0.1030846,"domain_scores_codex":[0.9986237,0.0002269478,0.0002919728,0.0003981452,0.0001562909,0.0003029153],"domain_scores_gemma":[0.9991943,0.0002877691,0.0001617425,0.0003031324,0.00000232781,0.00005070399],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003153395,0.00005992796,0.973541,0.00003146787,0.000009235766,0.00000806257,0.01592006,0.0001997486,0.0000360166,3.717838e-7,5.795687e-7,0.010162],"study_design_scores_gemma":[0.001087777,0.00009416298,0.7199561,0.00006177048,0.00004849655,0.0000245021,0.001647686,0.2769021,0.000001845667,0.00004069449,0.00001372862,0.000121114],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9987068,0.0003398005,0.00001236562,0.0001908352,0.00002016874,0.0006047687,0.00002893048,0.000007898621,0.00008846433],"genre_scores_gemma":[0.9994741,0.0001020963,0.00005640924,0.0001550261,0.00002151776,0.0001506234,0.00001349572,0.00001728545,0.000009419102],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2767023,"threshold_uncertainty_score":0.9991854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0817784419897449,"score_gpt":0.2860794661450175,"score_spread":0.2043010241552726,"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."}}