{"id":"W3205348916","doi":"10.1029/2021ef002399","title":"Scaling Deep Decarbonization Technologies","year":2021,"lang":"en","type":"article","venue":"Earth s Future","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canada Energy Regulator","funders":"National Academy of Sciences; Alfred P. Sloan Foundation","keywords":"Resource (disambiguation); Renewable energy; Business; Emerging technologies; Climate change mitigation; Climate change; Environmental economics; Natural resource economics; Environmental resource management; Environmental science; Economics; Engineering; Computer 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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00007288675,0.0000879226,0.00007663695,0.00001063614,0.0001151502,0.0000286736,0.00009591634,0.00009943765,0.001975119],"category_scores_gemma":[0.00007347333,0.00007870416,0.00003885689,0.0002456137,0.0001049556,0.0001581096,0.0001741799,0.0001236082,0.0002078319],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006445698,"about_ca_system_score_gemma":0.000006441575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000109607,"about_ca_topic_score_gemma":0.00009107286,"domain_scores_codex":[0.999294,0.00002871525,0.00009276385,0.0002156398,0.0001682316,0.0002006714],"domain_scores_gemma":[0.9996604,0.00001209702,0.00002429193,0.00025762,0.000004259986,0.00004130988],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00001012648,0.0001331184,0.6462912,0.00001606936,0.00001127332,0.00009458746,0.001009507,0.00235036,0.01312144,0.0004445149,0.000535906,0.3359819],"study_design_scores_gemma":[0.0002992518,0.00003527931,0.781824,0.000007092079,0.00001439546,0.00003724446,0.007842235,0.00138364,0.06271426,0.002830321,0.1426804,0.0003319413],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9873056,0.0005091506,0.001340274,0.00144239,0.0001667623,0.00008978041,0.000001461678,0.0001650401,0.008979505],"genre_scores_gemma":[0.9947979,0.00008591342,0.003764414,0.0001951913,0.00006234399,0.0000039683,0.00001106864,0.000008323234,0.001070824],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3356499,"threshold_uncertainty_score":0.9989372,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.002649214037660687,"score_gpt":0.1930568913208161,"score_spread":0.1904076772831554,"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."}}