{"id":"W4225913161","doi":"10.1017/eds.2022.2","title":"Evolution of machine learning in environmental science—A perspective","year":2022,"lang":"en","type":"article","venue":"Environmental Data Science","topic":"Hydrological Forecasting Using AI","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"Government of British Columbia; University of British Columbia","funders":"","keywords":"Parametrization (atmospheric modeling); Perspective (graphical); Computer science; Artificial intelligence; Artificial neural network; Machine learning; Climate science; Data assimilation; Deep learning; Convolutional neural network; General Circulation Model; Climate change; Physics; Meteorology; Ecology","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":[0.006208572,0.0007144129,0.0007389565,0.002520393,0.00089938,0.00429577,0.001000352,0.004148219,0.004269248],"category_scores_gemma":[0.007258131,0.000389123,0.000532011,0.001549457,0.009242037,0.007288598,0.002350887,0.006074988,0.0008903866],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005761169,"about_ca_system_score_gemma":0.001561074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003443403,"about_ca_topic_score_gemma":0.002001931,"domain_scores_codex":[0.9980136,0.001149622,0.00008077844,0.0003060466,0.0003322624,0.0001176259],"domain_scores_gemma":[0.9935341,0.0047521,0.0001836002,0.0002685193,0.0009088371,0.0003529854],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000254861,0.0000471029,0.000465459,0.00017894,0.00002407263,0.00005549702,0.0001980575,0.003144074,0.0001232045,0.9501883,0.01000035,0.03554944],"study_design_scores_gemma":[0.00001630456,0.00004620535,0.0008548207,0.0005271676,0.00000875856,0.0000864678,0.000191414,0.009612717,0.0002000486,0.8333678,0.155059,0.00002935064],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.01052712,0.4750949,0.05726572,0.3046512,0.005536781,0.00004295179,0.0002742071,0.0001504691,0.1464567],"genre_scores_gemma":[0.6071233,0.2585326,0.04661373,0.03021828,0.02844062,0.0002217473,0.0002547661,0.000189229,0.02840579],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.006208572,"threshold_uncertainty_score":0.04180044,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01709476070128623,"score_gpt":0.2403423857968685,"score_spread":0.2232476250955823,"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."}}