{"id":"W2071599933","doi":"10.1021/ef901212n","title":"Efficiency Analysis of Natural Gas Residential Micro-cogeneration Systems","year":2010,"lang":"en","type":"article","venue":"Energy & Fuels","topic":"Thermodynamic and Exergetic Analyses of Power and Cooling Systems","field":"Engineering","cited_by":31,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; University of New Brunswick; University of British Columbia","funders":"","keywords":"Exergy; Stirling engine; Cogeneration; Process engineering; Natural gas; Internal combustion engine; Environmental science; Spark-ignition engine; Primary energy; Combustion; Ignition system; Waste management; Electricity generation; Automotive engineering; Power (physics); Engineering; Renewable energy; Mechanical engineering; Chemistry; Thermodynamics; Electrical engineering","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":[0.0001355888,0.0001414954,0.0003237237,0.0002801822,0.00005634264,0.00004041392,0.0001917255,0.0001018171,0.00006779346],"category_scores_gemma":[0.000008961039,0.0001287238,0.0002153835,0.0004783062,0.00003618544,0.00006461438,0.00001532965,0.0001139134,0.000007566799],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001697788,"about_ca_system_score_gemma":0.00001484422,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007677897,"about_ca_topic_score_gemma":0.0004599501,"domain_scores_codex":[0.9990634,0.00002491952,0.0003542634,0.000165132,0.0001927271,0.0001995973],"domain_scores_gemma":[0.9994991,0.00002423964,0.00006606767,0.0002872696,0.00006836834,0.00005498081],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00000705411,0.00001570726,0.0001892562,0.00002365597,0.0006504728,0.000002822178,0.0001436411,0.06593605,0.9272748,0.004529105,0.0002644282,0.0009630544],"study_design_scores_gemma":[0.0004269578,0.0000382749,0.0018199,0.00003828657,0.001578305,0.00001425076,0.0002722355,0.7581817,0.2319943,0.0001306317,0.004905589,0.0005995654],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9447051,0.001628629,0.04876141,0.000006434813,0.002339321,0.00003083985,0.00001191006,0.0001073352,0.002409005],"genre_scores_gemma":[0.9987365,0.00004588372,0.00008730398,0.000006390489,0.0002582792,0.000007233786,0.00006020708,0.00001867946,0.0007794939],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6952804,"threshold_uncertainty_score":0.5249202,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003378869859466508,"score_gpt":0.198633531196146,"score_spread":0.1952546613366795,"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."}}