{"id":"W2008517337","doi":"10.1016/j.apenergy.2014.11.004","title":"An inverse method for calculation of thermal inertia and heat gain in air conditioning and refrigeration systems","year":2014,"lang":"en","type":"article","venue":"Applied Energy","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":false,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Air conditioning; Refrigeration; Thermodynamics; Thermal inertia; Inertia; Conditioning; Thermal; Inverse; Mechanics; Environmental science; Control theory (sociology); Mathematics; Physics; Computer science; Classical mechanics; Statistics; Artificial intelligence","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":[0.0001417834,0.00006358188,0.0001002385,0.00006544785,0.00003022716,0.000008297827,0.00001939825,0.00006817917,6.94774e-7],"category_scores_gemma":[0.000002998055,0.00006581232,0.000006651654,0.00005815053,0.00001049766,0.00008332398,0.000004456972,0.00002445199,2.162739e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001334046,"about_ca_system_score_gemma":0.000002825927,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001845817,"about_ca_topic_score_gemma":0.00009361489,"domain_scores_codex":[0.9996479,0.00002139938,0.0001312542,0.00009360581,0.00003614193,0.00006977093],"domain_scores_gemma":[0.999841,0.00003928631,0.00001873344,0.00006689192,0.00001199635,0.00002213384],"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":[0.000008497806,0.000004380732,0.00006769447,0.00002566735,0.000005147961,3.171938e-8,0.00008850397,0.8736969,0.05159258,0.07198775,0.000009121078,0.002513732],"study_design_scores_gemma":[0.0003171938,0.00002039854,0.0004085601,0.00001195401,0.000006018922,7.057932e-7,0.00002443203,0.9823965,0.0161861,0.0003966195,0.0001591638,0.00007227808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3675548,0.00002763764,0.6319211,0.000004753281,0.0000337528,0.00004430335,9.706923e-7,0.0000376365,0.0003750612],"genre_scores_gemma":[0.9871004,0.000008086914,0.01267318,0.00002829103,0.00003712459,0.00005525982,0.00007734398,0.00001350151,0.000006833874],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6195456,"threshold_uncertainty_score":0.2683749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005904381894036135,"score_gpt":0.2124553226145476,"score_spread":0.2065509407205114,"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."}}