{"id":"W4391226126","doi":"10.1016/j.energy.2024.130441","title":"Achieving China's ‘double carbon goals’, an analysis of the potential and cost of carbon capture in the resource-based area: Northwestern China","year":2024,"lang":"en","type":"article","venue":"Energy","topic":"Environmental Impact and Sustainability","field":"Environmental Science","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University; University of Waterloo","funders":"Xi’an Jiaotong University","keywords":"China; Greenhouse gas; Carbon fibers; Environmental science; Resource (disambiguation); Carbon sequestration; Natural resource economics; Climate change; Environmental resource management; Geography; Carbon dioxide; Economics; Ecology; 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":[],"consensus_categories":[],"category_scores_codex":[0.0003577553,0.0001400045,0.0002057443,0.00007339313,0.00005525777,0.00002736663,0.0002820319,0.00006935986,0.00005519511],"category_scores_gemma":[0.000008007183,0.00008227404,0.0001148014,0.0006287504,0.0002873691,0.00007260515,0.0001410048,0.0001482977,8.693075e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000139888,"about_ca_system_score_gemma":0.00001227859,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.06113353,"about_ca_topic_score_gemma":0.02088357,"domain_scores_codex":[0.9988006,0.0001915069,0.0002182327,0.0002672409,0.0003279812,0.0001944659],"domain_scores_gemma":[0.9994021,0.0000328157,0.00006899399,0.0004454245,0.000001692201,0.00004895924],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005078696,0.0001381794,0.8390595,0.00002058682,0.00005181418,0.00001333002,0.003778132,0.1493379,0.005864406,0.00004512091,0.000003495771,0.00163675],"study_design_scores_gemma":[0.0001790783,0.00004858577,0.950256,0.00001394561,0.0001710598,0.000001882917,0.0008093953,0.04707491,0.001142327,0.00003797003,0.0001747446,0.00009004454],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9964757,0.000217867,0.00002834693,0.0002460704,0.00003153686,0.0001244373,0.00001150301,0.000008855787,0.002855685],"genre_scores_gemma":[0.9997343,0.000009982932,0.000007523817,0.00005574656,0.00001328321,0.00000990527,0.00001630834,0.00001025882,0.0001426356],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1111965,"threshold_uncertainty_score":0.9969828,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00470811444234688,"score_gpt":0.2081190892083742,"score_spread":0.2034109747660273,"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."}}