{"id":"W4393360369","doi":"10.1007/978-981-97-1316-5_9","title":"Machine Learning (ML) in Water Resources","year":2024,"lang":"en","type":"book-chapter","venue":"Water science and technology library","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005091858,0.0007354383,0.0008547478,0.001086367,0.0003254718,0.001864159,0.0007516321,0.001146303,0.02060051],"category_scores_gemma":[0.001895666,0.0004409754,0.0003719123,0.003113495,0.001189966,0.003655978,0.001061519,0.002309947,0.01032111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007383099,"about_ca_system_score_gemma":0.0006917709,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001480851,"about_ca_topic_score_gemma":0.002106372,"domain_scores_codex":[0.9996685,0.00008687223,0.00001881414,0.00005729181,0.0001541516,0.00001438344],"domain_scores_gemma":[0.9990668,0.0007453288,0.00002754924,0.0000815028,0.00006298919,0.00001589784],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001315456,0.00004412553,0.0001222826,0.0005834274,0.00001772488,0.00003531066,0.00008604087,0.009149674,0.0005917827,0.245046,0.1705629,0.5737476],"study_design_scores_gemma":[0.000006921216,0.00002725365,0.0003265799,0.0003078915,0.00001372793,0.0001089402,0.00004091648,0.03987378,0.001172528,0.4835405,0.4745594,0.00002150163],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.001815463,0.1501603,0.5047765,0.01126041,0.004216266,0.00004811316,0.0006419042,0.002450268,0.3246308],"genre_scores_gemma":[0.06114474,0.1592646,0.1931179,0.00474263,0.01000465,0.0002595776,0.001071443,0.001408605,0.5689859],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.02060051,"threshold_uncertainty_score":0.06891555,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007508778024634585,"score_gpt":0.1782687040744809,"score_spread":0.1707599260498464,"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."}}