{"id":"W4321996236","doi":"10.5194/egusphere-egu23-10575","title":"Machine learning and hydrological sciences: A systematic overview  of review papers","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Context (archaeology); Computer science; Artificial intelligence; Systematic review; Scope (computer science); Data science; Management science; Machine learning; Geography; Engineering; Political science","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.01084245,0.002036497,0.004833429,0.03145001,0.0008476744,0.00351461,0.001776715,0.001904621,0.004821203],"category_scores_gemma":[0.03509413,0.001091785,0.005261803,0.02923767,0.0007395257,0.004566857,0.002095642,0.001423885,0.0008545475],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004493311,"about_ca_system_score_gemma":0.01564885,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00551592,"about_ca_topic_score_gemma":0.01631166,"domain_scores_codex":[0.9929982,0.002010518,0.002612726,0.0005590402,0.00154105,0.000278367],"domain_scores_gemma":[0.9726924,0.01669072,0.005413318,0.0003419748,0.004472208,0.0003893536],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0001726023,0.0000299173,0.0009187507,0.7877863,0.002531883,0.0003358588,0.0005336718,0.0002813842,0.0005098765,0.001536422,0.0116904,0.193673],"study_design_scores_gemma":[0.00005710164,0.0001671928,0.002749853,0.8379315,0.01071288,0.0007241142,0.0005339278,0.0001312021,0.0002905076,0.001274261,0.1453764,0.00005103662],"study_design_candidate":"systematic_review","study_design_consensus":"systematic_review","genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002549647,0.9982049,0.0002468863,0.0004570029,0.0001597099,0.0001415684,0.0002232856,0.000007840946,0.0003037881],"genre_scores_gemma":[0.001746342,0.9963483,0.0008298717,0.0003815204,0.0001134647,0.0002823801,0.0001645247,0.00000461235,0.0001290685],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9891576,"threshold_uncertainty_score":0.05734098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1189492935018537,"score_gpt":0.3269426250879993,"score_spread":0.2079933315861456,"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."}}