{"id":"W4315780269","doi":"10.1039/d2va00236a","title":"Prospects of carbon capture, utilization and storage for mitigating climate change","year":2023,"lang":"en","type":"article","venue":"Environmental Science Advances","topic":"Carbon Dioxide Capture Technologies","field":"Engineering","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Ontario Ministry of Economic Development, Job Creation and Trade; Ministero dello Sviluppo Economico; Agriculture and Agri-Food Canada; Ministry of Agriculture, Food and Rural Affairs; Ontario Ministry of Economic Development and Innovation; Ontario Ministry of Agriculture, Food and Rural Affairs","keywords":"Climate change; Carbon capture and storage (timeline); Scale (ratio); Environmental science; Environmental resource management; Climate change mitigation; Environmental economics; Risk analysis (engineering); Environmental planning; Business; Geography; Economics; Ecology","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":[],"consensus_categories":[],"category_scores_codex":[0.0001360373,0.00008402704,0.00009272246,0.00009666702,0.00003859816,0.00001076768,0.000112382,0.00002899672,0.000001376846],"category_scores_gemma":[0.00002951873,0.00008061748,0.00001448605,0.0003069204,0.0003982496,0.0003122644,0.0000717098,0.0000394849,9.364759e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000597941,"about_ca_system_score_gemma":0.000001971279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002619096,"about_ca_topic_score_gemma":0.000005993521,"domain_scores_codex":[0.9993245,0.000002182195,0.00009979202,0.0001779319,0.0001645942,0.0002310189],"domain_scores_gemma":[0.9998038,0.00002463258,0.00003464856,0.000105543,0.000002539,0.00002885331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000003659288,0.00001030559,0.01658615,0.000199815,0.000003853162,0.000003453172,0.001057223,0.00373669,0.9315791,0.0008805253,0.000005396435,0.04593386],"study_design_scores_gemma":[0.0003431139,0.0000980635,0.1072431,0.00009247431,0.00001255931,0.000005789574,0.003441409,0.06708176,0.8186582,0.001776076,0.0008920771,0.0003553644],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9953864,0.003412165,0.00004138341,0.00002047545,0.0001391465,0.0003192127,0.00002496499,0.0003005359,0.0003557836],"genre_scores_gemma":[0.9977159,0.001349459,0.0008079624,0.000006411298,0.00001720961,0.00008255032,0.000004568699,0.00001146618,0.000004455147],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1129209,"threshold_uncertainty_score":0.3287485,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01722385833239109,"score_gpt":0.2375630034433588,"score_spread":0.2203391451109677,"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."}}