{"id":"W4205588374","doi":"10.36227/techrxiv.14398304","title":"Predicting Rainfall using Machine Learning Techniques","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Energy Load and Power Forecasting","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Computer science; Reliability (semiconductor); Machine learning; Set (abstract data type); Artificial intelligence","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"],"consensus_categories":[],"category_scores_codex":[0.000250211,0.0003684886,0.0003783255,0.0001510163,0.0001068453,0.000192323,0.0002113301,0.0003871187,0.0002087932],"category_scores_gemma":[0.00006876414,0.0003924241,0.0001638745,0.0001308617,0.00001866583,0.00009921855,0.0005410857,0.001473335,0.000002178089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000113578,"about_ca_system_score_gemma":0.00004455353,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007218301,"about_ca_topic_score_gemma":0.0001985492,"domain_scores_codex":[0.9986786,0.00004567544,0.0003855771,0.0003406449,0.0001849118,0.0003646139],"domain_scores_gemma":[0.9994306,0.00005936041,0.00007569183,0.0002914337,0.00005637535,0.00008656467],"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":[0.000002296742,0.0000136176,0.01752099,0.0007921204,0.0002228986,0.00007695157,0.0009155043,0.9475092,0.02234727,0.00008296805,0.00003618913,0.01048004],"study_design_scores_gemma":[0.00006145611,0.000009325893,0.00003402311,0.0008784642,0.00004102656,0.00003878182,0.00009517669,0.9407292,0.05412025,0.0000459574,0.003452738,0.0004936089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7957321,0.004449815,0.09017236,0.00001789056,0.001890481,0.0002361391,0.00001606299,0.006594515,0.1008906],"genre_scores_gemma":[0.8985537,0.0003553613,0.0994475,0.00003922227,0.0006427781,0.00002047064,0.0001956397,0.0001679221,0.0005774668],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1028215,"threshold_uncertainty_score":0.9998528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0185284624655089,"score_gpt":0.2325997206975775,"score_spread":0.2140712582320685,"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."}}