{"id":"W4288789584","doi":"10.1016/b978-0-323-85585-3.00004-3","title":"Application of calcium looping (CaL) technology for CO2 capture","year":2022,"lang":"en","type":"book-chapter","venue":"Elsevier eBooks","topic":"Chemical Looping and Thermochemical Processes","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Carbonation; Calcium looping; Sorbent; Materials science; Process engineering; Attrition; Process (computing); Chemical engineering; Nanotechnology; Biochemical engineering; Chemistry; Computer science; Engineering; Adsorption; Organic chemistry","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009002395,0.0003443158,0.0001678594,0.0005216858,0.0002241317,0.0007535503,0.0003992093,0.000521786,0.004184453],"category_scores_gemma":[0.0001158652,0.000144522,0.0002853239,0.0007308122,0.0002685842,0.0006849892,0.0003667028,0.000555585,0.0007783084],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006335941,"about_ca_system_score_gemma":0.0003394642,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001482339,"about_ca_topic_score_gemma":0.003009661,"domain_scores_codex":[0.9999113,0.000003062606,0.000003060224,0.00002491181,0.00004740149,0.00001019235],"domain_scores_gemma":[0.999966,0.00001309467,0.000004084331,0.000004032719,0.000009745857,0.000002987417],"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":[0.0001279593,0.00007989439,0.0002730894,0.0009961237,0.00001242717,0.0002732473,0.00008240049,0.001500813,0.7461053,0.01431879,0.00370757,0.2325223],"study_design_scores_gemma":[0.00002136935,0.0001858225,0.0009075029,0.00006899347,0.00002548334,0.0004963743,0.00005747345,0.005172717,0.7831806,0.002546398,0.2073177,0.00001947534],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"other","genre_gemma":"empirical","genre_scores_codex":[0.2638712,0.1248556,0.1094066,0.002428485,0.002212041,0.0002936872,0.0006539621,0.001176687,0.4951017],"genre_scores_gemma":[0.7130228,0.055171,0.04052125,0.0006636326,0.0002753587,0.000114568,0.0003669946,0.0001494774,0.1897149],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004184453,"threshold_uncertainty_score":0.01399839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01012689629342482,"score_gpt":0.2213011384120452,"score_spread":0.2111742421186204,"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."}}