{"id":"W2133771653","doi":"10.1109/ccece.2007.373","title":"Optimization of a Fuel-Cell EV Air-Conditioning System","year":2007,"lang":"en","type":"article","venue":"","topic":"Refrigeration and Air Conditioning Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Air conditioning; Automotive engineering; Fuel cells; Conditioning; Work (physics); Efficient energy use; Electric vehicle; Energy (signal processing); Computer science; Environmental science; Engineering; Electrical engineering; Mechanical engineering; Power (physics)","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":[0.0001152113,0.00007006073,0.00008877437,0.0001432824,0.00003683539,0.00001285789,0.00006672411,0.00007295908,0.00009804525],"category_scores_gemma":[0.00001054384,0.00006811306,0.00002958395,0.0001777334,0.00002085522,0.0001197024,0.000006516138,0.00005895113,0.00002459167],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000495166,"about_ca_system_score_gemma":0.000006082382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000034151,"about_ca_topic_score_gemma":0.000002521698,"domain_scores_codex":[0.9995097,0.000004233346,0.0002163103,0.00007169529,0.00008582634,0.0001122309],"domain_scores_gemma":[0.9997499,0.00002580385,0.00003468261,0.0001229922,0.00004570894,0.00002088588],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000001730419,0.00001082888,0.0001405519,0.0002223667,0.00001233329,0.000003309164,0.00008178217,0.9751887,0.006257005,0.01654715,0.001162173,0.0003720859],"study_design_scores_gemma":[0.0005807585,0.00005870676,0.0007574132,0.0001254517,0.00001802955,0.00001635203,0.002150015,0.5548993,0.4393575,0.0002673362,0.001451319,0.00031778],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02414018,0.0001071868,0.8255471,0.0000248727,0.0001629858,0.00008798079,0.000003390446,0.001707886,0.1482184],"genre_scores_gemma":[0.9683338,0.00001217395,0.03132175,0.0000120641,0.00001998552,0.000005259459,0.00002271566,0.00001217135,0.0002600286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9441937,"threshold_uncertainty_score":0.277757,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005128532577888084,"score_gpt":0.1906735908453096,"score_spread":0.1855450582674215,"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."}}