{"id":"W4404450183","doi":"10.1016/j.cej.2024.157722","title":"An advanced design to generate power and hydrogen with CO2 capturing and storage for cleaner applications","year":2024,"lang":"en","type":"article","venue":"Chemical Engineering Journal","topic":"Spacecraft and Cryogenic Technologies","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ontario Institute of Technology","funders":"","keywords":"Hydrogen storage; Process engineering; Waste management; Power to gas; Hydrogen; Power (physics); Environmental science; Chemistry; Engineering; Computer science; Thermodynamics; Physics; Organic chemistry; Electrolysis","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":[0.0001256965,0.0004122768,0.0003137029,0.0002636487,0.0002051602,0.0004958414,0.0006301536,0.0004620119,0.003906068],"category_scores_gemma":[0.0001316018,0.0001814605,0.0003037164,0.0002158025,0.0001338157,0.0004489289,0.0002094703,0.0002781598,0.001068164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003047254,"about_ca_system_score_gemma":0.0003880668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005833746,"about_ca_topic_score_gemma":0.0009376509,"domain_scores_codex":[0.9998792,0.00001112175,0.000005396209,0.00002896601,0.00006223679,0.00001305969],"domain_scores_gemma":[0.9999268,0.00000716495,0.00001372487,0.000009540051,0.00003543646,0.000007378141],"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":[0.000392314,0.0001743891,0.001099193,0.000633759,0.00005853316,0.0001964256,0.00004642425,0.03307226,0.8753715,0.003128348,0.002493332,0.08333345],"study_design_scores_gemma":[0.0001548881,0.002649212,0.005790042,0.00003436852,0.00018213,0.0007259593,0.00004785035,0.2642398,0.6395451,0.001465839,0.08509871,0.00006611425],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4377804,0.001425262,0.5123685,0.0003593063,0.0003171622,0.0006099116,0.001062116,0.002912288,0.04316511],"genre_scores_gemma":[0.9035097,0.0004991126,0.07472617,0.00009901246,0.00003174849,0.0002760247,0.0004608555,0.0001104677,0.02028681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003906068,"threshold_uncertainty_score":0.01306713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005537253651118338,"score_gpt":0.1989104558917204,"score_spread":0.1933732022406021,"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."}}