{"id":"W6968720188","doi":"10.5281/zenodo.5517557","title":"A Comprehensive Comparative Analysis of Machine Learning Models for Predicting Heating and Cooling Loads","year":2021,"lang":"en","type":"book-chapter","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Scientific and Engineering Research Topics","field":"Dentistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial neural network; Support vector machine; Mean squared error; Approximation error; Radial basis function; Mean absolute percentage error; Energy consumption; Kernel (algebra)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001508576,0.001364164,0.0009679787,0.001368773,0.0002222099,0.0009455601,0.00073389,0.0008272854,0.002204282],"category_scores_gemma":[0.003172568,0.000225657,0.001035357,0.002080656,0.0001403058,0.001021145,0.0002546286,0.000655181,0.001140223],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004702559,"about_ca_system_score_gemma":0.0004695345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003922551,"about_ca_topic_score_gemma":0.003365743,"domain_scores_codex":[0.9993694,0.0001925684,0.00003453556,0.00008147658,0.0002963025,0.00002575422],"domain_scores_gemma":[0.9985602,0.001067007,0.00003983792,0.00006426317,0.0002555719,0.00001302757],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000211722,0.0001591838,0.004365585,0.001213976,0.0003044315,0.0001328817,0.00007983939,0.3470868,0.002661581,0.005681871,0.01155556,0.6265466],"study_design_scores_gemma":[0.000008134263,0.0003186238,0.006171119,0.0002308551,0.0001663356,0.0001582811,0.00007062734,0.9705401,0.00367998,0.003718693,0.01488706,0.00005013169],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"review","genre_scores_codex":[0.1298352,0.1504644,0.6552169,0.001353865,0.001444859,0.0002194261,0.00221905,0.00355794,0.05568834],"genre_scores_gemma":[0.5792137,0.07847727,0.2993721,0.0002936444,0.0007066798,0.0002909785,0.006395841,0.000512859,0.03473691],"genre_candidate":"review","genre_consensus":null,"teacher_disagreement_score":0.003922551,"threshold_uncertainty_score":0.007978201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09183560593990077,"score_gpt":0.2843493602870238,"score_spread":0.1925137543471231,"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."}}