{"id":"W2804161861","doi":"10.14741/ijcet/v.8.3.7","title":"Prediction of Monsoon Rain for the Year 2018 for Marathawada India","year":2018,"lang":"en","type":"article","venue":"International Journal of Current Engineering and Technology","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Fast Fourier transform; Monsoon; Rain rate; Meteorology; Environmental science; Statistical analysis; Climatology; Regression analysis; Reliability (semiconductor); Time series; Mathematics; Statistics; Geography; Geology; Algorithm; Precipitation; Physics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001650684,0.0004003916,0.0001834834,0.0006146389,0.0002077408,0.0004380441,0.0003096032,0.0002600887,0.0005174747],"category_scores_gemma":[0.0003354734,0.0001350997,0.0003463327,0.0005190048,0.00009856148,0.0002589671,0.0001616578,0.0003116956,0.0002552246],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004953433,"about_ca_system_score_gemma":0.0005337689,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0387831,"about_ca_topic_score_gemma":0.05800063,"domain_scores_codex":[0.9999378,0.00001035942,0.00000479187,0.00001544055,0.00001211535,0.00001954073],"domain_scores_gemma":[0.9998517,0.00003415652,0.00003119456,0.00001216229,0.00004805862,0.00002273176],"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":[0.0005506397,0.0002920016,0.6536039,0.0002584483,0.0002196984,0.00136201,0.0004802907,0.2674665,0.01530551,0.0007227765,0.004899507,0.05483888],"study_design_scores_gemma":[0.00004207709,0.000285654,0.603407,0.00002650797,0.00009281123,0.0002055426,0.0007431292,0.3853153,0.00646227,0.00022309,0.003145951,0.0000507279],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964225,0.0001201464,0.001298262,0.00009509497,0.00001988899,0.000009532073,0.001197999,0.0001668453,0.0006697687],"genre_scores_gemma":[0.9967611,0.00014216,0.0009486628,0.000006415543,0.000009388609,0.000008416039,0.001644994,0.00001202014,0.0004668976],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0387831,"threshold_uncertainty_score":0.0771147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01034941740912164,"score_gpt":0.2419312293330363,"score_spread":0.2315818119239147,"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."}}