{"id":"W4387775969","doi":"10.3390/en16207149","title":"Transfer Learning Prediction Performance of Chillers for Neural Network Models","year":2023,"lang":"en","type":"article","venue":"Energies","topic":"Building Energy and Comfort Optimization","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; Concordia University","keywords":"Chiller; Artificial neural network; Transfer of learning; Air conditioning; Computer science; Machine learning; Automation; Chiller boiler system; Water chiller; Artificial intelligence; Simulation; Engineering; Mechanical engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[{"model":"gemma","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"},{"model":"gpt","categories":[],"domain":null,"study_design":"simulation_or_modeling","genre":"empirical","about_ca_system":false,"about_ca_topic":false,"confidence":"low","status":"direct model label, unvalidated"}],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002067681,0.001259424,0.0005549169,0.0004205345,0.0002748249,0.0005538769,0.0006960001,0.001143698,0.001010545],"category_scores_gemma":[0.00609048,0.0002319375,0.0005041463,0.0002928411,0.0004739331,0.0009073936,0.000657953,0.001385427,0.0002271843],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001142279,"about_ca_system_score_gemma":0.0007379305,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02127198,"about_ca_topic_score_gemma":0.01161823,"domain_scores_codex":[0.9996641,0.0001183137,0.00002433918,0.00008363472,0.00005007439,0.00005966886],"domain_scores_gemma":[0.9976075,0.001620908,0.0001595465,0.0001523231,0.0003801051,0.0000795732],"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":[0.0001099523,0.00006290051,0.002857367,0.00002389465,0.00002353522,0.00001589863,0.00001376253,0.9849271,0.0005017798,0.0001866427,0.0002539672,0.01102324],"study_design_scores_gemma":[0.000002007336,0.00002091489,0.000526141,0.000002215072,0.000002100759,0.00000146352,0.000004162626,0.9988562,0.0004415036,0.000124674,0.0000163674,0.000002356976],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9396167,0.0007763096,0.05462287,0.0005191599,0.00007444173,0.00004617804,0.0002382642,0.0006190815,0.003486895],"genre_scores_gemma":[0.9965143,0.00005276008,0.002708056,0.00002327829,0.000007041603,0.00001543714,0.0001828599,0.00001026632,0.0004860637],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02127198,"threshold_uncertainty_score":0.04229629,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01130565277584235,"score_gpt":0.1788660646893792,"score_spread":0.1675604119135369,"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."}}