{"id":"W4402968940","doi":"10.1016/j.heliyon.2024.e37965","title":"Daily river flow simulation using ensemble disjoint aggregating M5-Prime model","year":2024,"lang":"en","type":"article","venue":"Heliyon","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":72,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Prince Edward Island","funders":"Natural Environment Research Council; Ministry of Environment; Korea Environmental Industry and Technology Institute; Ministry of Education - Singapore; University of Warwick","keywords":"Prime (order theory); Disjoint sets; Flow (mathematics); Mathematics; Computer science; Hydrology (agriculture); Geology; Geotechnical engineering; Combinatorics; Geometry","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":[0.0006178849,0.0005270786,0.0005238611,0.0003288171,0.0003110351,0.000510579,0.0007858512,0.0006656798,0.0006634966],"category_scores_gemma":[0.001138374,0.0002173713,0.0006611919,0.0003613819,0.0002619277,0.0004520536,0.0004854697,0.0006790083,0.0001174481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005436932,"about_ca_system_score_gemma":0.0007607416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04205729,"about_ca_topic_score_gemma":0.02840216,"domain_scores_codex":[0.9998679,0.00004161277,0.000006522636,0.00003714234,0.00002326593,0.00002354377],"domain_scores_gemma":[0.9996787,0.000132396,0.00003989316,0.00003509088,0.00008729901,0.00002652307],"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":[0.00002667625,0.00001876753,0.002638839,0.000007041274,0.00001494932,0.00001970603,0.00000919591,0.9922885,0.0002260585,0.0002820923,0.0002165041,0.004251625],"study_design_scores_gemma":[0.000001114534,0.000003205979,0.0001802392,4.13078e-7,0.00000128369,8.451032e-7,0.000001464221,0.9997078,0.0000393249,0.00004003789,0.00002346219,7.497002e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8776476,0.0003133516,0.1150122,0.00043554,0.00007426806,0.00004888861,0.001027187,0.0007035211,0.004737324],"genre_scores_gemma":[0.9890777,0.00006205666,0.009713253,0.00003696014,0.00001371582,0.00002855517,0.0004590731,0.00001056706,0.0005981412],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04205729,"threshold_uncertainty_score":0.08362496,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04387888383445709,"score_gpt":0.2833870084308386,"score_spread":0.2395081245963815,"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."}}