{"id":"W2950398529","doi":"10.1145/3485128","title":"Tackling Climate Change with Machine Learning","year":2022,"lang":"en","type":"preprint","venue":"ACM Computing Surveys","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":193,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Pacific Northwest National Laboratory; Lawrence Livermore National Laboratory; University of California, Davis; Universitetet i Oslo; Carnegie Mellon University; University College London; Eidgenössische Technische Hochschule Zürich; Dalhousie University; National Science Foundation; Imperial College London; Natural Sciences and Engineering Research Council of Canada; DeepMind; Université du Québec à Rimouski; University of Colorado Boulder; Universität Zürich; University of Pennsylvania; Yale University; U.S. Department of Energy","keywords":"Climate change; Wonder; Computer science; Artificial intelligence; Greenhouse gas; Humanity; Machine learning; Data science; Political science; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004473992,0.0008983733,0.001169134,0.001396384,0.0008919711,0.004128189,0.001785244,0.002742924,0.005378965],"category_scores_gemma":[0.0169044,0.0004122721,0.000878419,0.002157066,0.001434821,0.008077549,0.002397401,0.005591712,0.001575724],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001251006,"about_ca_system_score_gemma":0.001324921,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003358078,"about_ca_topic_score_gemma":0.003570406,"domain_scores_codex":[0.9979023,0.001110481,0.00006643494,0.0003175442,0.0004421073,0.00016128],"domain_scores_gemma":[0.9909942,0.007190076,0.0002694559,0.0007446239,0.0005746788,0.0002268862],"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":[0.00008353678,0.0002648355,0.006553226,0.0009076954,0.0004024407,0.0001090496,0.000286119,0.286258,0.001017719,0.1444486,0.06804656,0.4916221],"study_design_scores_gemma":[0.00001499054,0.00003578492,0.0008473475,0.0001346437,0.00004141543,0.00003114247,0.000163356,0.5178747,0.0007219143,0.4221078,0.05799246,0.00003436641],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.01531005,0.02684291,0.8626582,0.06767361,0.002476203,0.0001373194,0.0009012856,0.002868803,0.0211316],"genre_scores_gemma":[0.5131738,0.04787597,0.404571,0.01033539,0.009391597,0.0004411646,0.002739505,0.0005836165,0.01088795],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.005378965,"threshold_uncertainty_score":0.02366108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06174498602231311,"score_gpt":0.292308745863756,"score_spread":0.2305637598414429,"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."}}