{"id":"W3043443854","doi":"10.1109/tase.2020.3005888","title":"Development of Inverse Greybox Model-Based Virtual Meters for Air Handling Units","year":2020,"lang":"en","type":"article","venue":"IEEE Transactions on Automation Science and Engineering","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Metering mode; Set (abstract data type); Engineering; Computer science; Component (thermodynamics); Industrial engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0008631377,0.0006373039,0.0006840471,0.0005375553,0.0003177995,0.001098137,0.00115221,0.0007189908,0.002402173],"category_scores_gemma":[0.002613725,0.0005650045,0.0008708004,0.0003249066,0.0003712247,0.0008947512,0.000961469,0.00104964,0.0005211071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009289197,"about_ca_system_score_gemma":0.001254212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006796331,"about_ca_topic_score_gemma":0.006022077,"domain_scores_codex":[0.9996582,0.00008373622,0.00002219458,0.00006953069,0.0001432357,0.00002305508],"domain_scores_gemma":[0.9993161,0.0003377477,0.00007024203,0.00006436047,0.0001824261,0.00002909545],"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":[0.00002066951,0.00001984093,0.0006906185,0.00003988011,0.00001657252,0.00003622617,0.00005026786,0.9641317,0.002656953,0.004839968,0.0003073113,0.02718998],"study_design_scores_gemma":[0.000001999864,0.000006067792,0.00004903327,0.000002620524,0.00000163137,0.000003456905,0.000004086868,0.9982291,0.0005514582,0.0007535688,0.0003942625,0.000002730822],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.008741215,0.00003104915,0.9887183,0.00004891495,0.00001989778,0.00004909787,0.00004929215,0.0007947044,0.001547537],"genre_scores_gemma":[0.3773405,0.00008639962,0.6197594,0.0000462861,0.00001131021,0.0002209359,0.0001896975,0.0001775709,0.002167978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006796331,"threshold_uncertainty_score":0.01351357,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02727807604650699,"score_gpt":0.2130193158900296,"score_spread":0.1857412398435226,"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."}}