{"id":"W2982653624","doi":"10.36227/techrxiv.14398304.v1","title":"Predicting Rainfall using Machine Learning Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Flood Risk Assessment and Management","field":"Environmental Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Machine learning; Reliability (semiconductor); Artificial intelligence; Set (abstract data type)","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004388335,0.0002640584,0.000247001,0.00005383066,0.0001738408,0.0001865268,0.0003269287,0.0001657214,0.004312526],"category_scores_gemma":[0.00002843423,0.0002460282,0.0001230338,0.0001199798,0.00005714953,0.0001489083,0.004773546,0.0006911963,0.0000304187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002701453,"about_ca_system_score_gemma":0.00001878512,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01349566,"about_ca_topic_score_gemma":0.001649744,"domain_scores_codex":[0.9983355,0.00009646337,0.0002804798,0.0005919227,0.0003948401,0.0003007362],"domain_scores_gemma":[0.9993721,0.00002132762,0.000170623,0.0003564318,0.0000078023,0.00007167154],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008526592,0.0001897255,0.8953647,0.0001908728,0.0001653938,0.0000985061,0.0008485479,0.06378053,0.01692877,0.00009811591,0.0007738554,0.02155248],"study_design_scores_gemma":[0.0003943442,0.0001158471,0.01601667,0.0004745206,0.000321585,0.00001633408,0.001154855,0.9053105,0.02791417,0.0007169019,0.04588328,0.00168104],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7768632,0.0002552744,0.05207774,0.0002712866,0.0005265207,0.0008754784,0.000003802493,0.0009517087,0.168175],"genre_scores_gemma":[0.7006503,0.0004044047,0.2901956,0.000322402,0.0001687769,0.00005918996,0.0001244718,0.00006473532,0.008010014],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.879348,"threshold_uncertainty_score":0.9999992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01691491611135579,"score_gpt":0.2658512421979068,"score_spread":0.2489363260865511,"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."}}