{"id":"W4210916416","doi":"10.1145/3485128","title":"Tackling Climate Change with Machine Learning","year":2022,"lang":"en","type":"review","venue":"OPUS 4 (Zuse Institute Berlin)","topic":"Air Quality Monitoring and Forecasting","field":"Environmental Science","cited_by":863,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; McGill University; Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Energy","keywords":"Climate change; Wonder; Computer science; Greenhouse gas; Join (topology); Humanity; Global warming; Data science; Political science","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008517818,0.0006729437,0.001215536,0.0001227933,0.00112958,0.0001221774,0.0006607193,0.0002301497,0.001121906],"category_scores_gemma":[0.00009623332,0.0005504418,0.0003195203,0.0006736512,0.0002452958,0.0005490488,0.0009345023,0.00155895,0.0007541943],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006037408,"about_ca_system_score_gemma":0.00005272965,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009522638,"about_ca_topic_score_gemma":0.0001054139,"domain_scores_codex":[0.9966751,0.0002449172,0.0006749777,0.0009108115,0.0006908934,0.0008033018],"domain_scores_gemma":[0.9984384,0.0001425153,0.0006282389,0.0005653563,0.000006568338,0.0002189149],"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":[0.000009314776,0.00005396375,0.0004505677,0.001758191,0.00004843101,0.0001805683,0.0003834656,0.0005163679,1.747773e-7,0.00007930759,0.00002781174,0.9964918],"study_design_scores_gemma":[0.0001820459,0.0001364456,0.000009407754,0.003200695,0.0003202944,0.0001816522,0.00003345555,0.0003751865,5.184461e-7,0.000004772213,0.9948555,0.0007000662],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002362062,0.987111,0.000117237,0.00002995692,0.001552865,0.000976839,0.00008474819,0.0003808265,0.009510363],"genre_scores_gemma":[0.00006919361,0.995308,0.001820739,0.00004846162,0.000687629,0.0003947733,0.0003953528,0.0001394392,0.001136421],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.9957918,"threshold_uncertainty_score":0.9997912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1124073879609631,"score_gpt":0.3189045354384443,"score_spread":0.2064971474774812,"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."}}