{"id":"W4411401796","doi":"10.1007/978-3-031-92605-1_20","title":"Comparing Traditional Machine Learning with Deep Learning: Finding the Optimal Tool for Precipitation Intensity Forecasting","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; University of Ottawa; Université Laval; Agriculture and Agri-Food Canada","funders":"","keywords":"Artificial intelligence; Precipitation; Machine learning; Computer science; Intensity (physics); Meteorology; Geography; Physics; Optics","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":[],"consensus_categories":[],"category_scores_codex":[0.0006052238,0.000243161,0.0004224857,0.00008732338,0.0005887146,0.0001643888,0.0001068236,0.0002309702,0.00005599567],"category_scores_gemma":[0.0002075125,0.0001495249,0.00006549031,0.00006585681,0.00007720936,0.00006211016,0.00001403768,0.000848203,7.649422e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000013696,"about_ca_system_score_gemma":0.00001657202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001062431,"about_ca_topic_score_gemma":0.0007675753,"domain_scores_codex":[0.9988123,0.00009279974,0.0003433813,0.0003292199,0.0001647666,0.0002575054],"domain_scores_gemma":[0.9963775,0.003209397,0.0002211896,0.000082866,0.00006611745,0.00004291597],"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":[0.0001212408,0.000001786906,0.07256204,0.00005243003,0.00004272065,0.00000284479,0.000157712,0.9127362,1.152992e-7,0.001228889,0.000005066379,0.01308892],"study_design_scores_gemma":[0.0002520875,0.0002346615,0.007198038,0.0002728762,0.00004841506,0.00001619714,0.0000200283,0.988305,5.373713e-8,0.001973559,0.001485367,0.0001936734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04537884,0.01061844,0.9115534,0.0001726021,0.0009602819,0.001924849,0.00005009029,0.00009562081,0.02924589],"genre_scores_gemma":[0.9965133,0.00005262839,0.0006773524,0.00005542093,0.0004306508,0.000008856017,0.0007407211,0.000008309151,0.001512736],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9511345,"threshold_uncertainty_score":0.6097449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05700198092612747,"score_gpt":0.2203403916996579,"score_spread":0.1633384107735305,"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."}}