{"id":"W4385665154","doi":"10.1007/s11069-023-06078-8","title":"An Australian convective wind gust climatology using Bayesian hierarchical modelling","year":2023,"lang":"en","type":"article","venue":"Natural Hazards","topic":"Climate variability and models","field":"Environmental Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"Australian Research Council; Natural Sciences and Engineering Research Council of Canada","keywords":"Climatology; Wind shear; Convective available potential energy; Environmental science; Meteorology; Natural hazard; Wind speed; Convective storm detection; Geography; Storm; Convection; Geology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001879422,0.0005039935,0.0007296416,0.001595927,0.0006528896,0.001346523,0.00150916,0.0008241247,0.002346126],"category_scores_gemma":[0.003791962,0.0007776331,0.001422095,0.001293583,0.0003774128,0.0009953442,0.00101347,0.001046553,0.0003625494],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001868194,"about_ca_system_score_gemma":0.001647037,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2040148,"about_ca_topic_score_gemma":0.2065709,"domain_scores_codex":[0.9994248,0.0002156183,0.0000384769,0.0001533314,0.00009509766,0.00007275658],"domain_scores_gemma":[0.9989187,0.0004889061,0.0001815667,0.00006545309,0.0002523205,0.00009308706],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00005385222,0.00007675149,0.01790863,0.00005223614,0.0001359282,0.00009945082,0.0001873566,0.9546069,0.0004240275,0.004846365,0.001705153,0.01990334],"study_design_scores_gemma":[0.000003413645,0.000004756234,0.002342473,0.000005609351,0.000006410713,0.000003638782,0.00001207518,0.9962491,0.00001730638,0.001128437,0.0002199941,0.000006791725],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6706462,0.0009530434,0.3083019,0.001607613,0.0001226762,0.0003728074,0.004540647,0.001120428,0.01233472],"genre_scores_gemma":[0.9500554,0.0002388386,0.04335266,0.0001191392,0.00004305358,0.0001047806,0.002204058,0.000085402,0.0037967],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2040148,"threshold_uncertainty_score":0.4056547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03594659306373682,"score_gpt":0.3087768764035622,"score_spread":0.2728302833398253,"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."}}