{"id":"W4402537304","doi":"10.1016/j.apenergy.2024.124430","title":"Computationally effective machine learning approach for modular thermal energy storage design","year":2024,"lang":"en","type":"article","venue":"Applied Energy","topic":"Phase Change Materials Research","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Prince Edward Island; University of Toronto","funders":"National Research Council Canada; Natural Sciences and Engineering Research Council of Canada; National Research Council","keywords":"Modular design; Computer science; Thermal energy storage; Thermal; Energy storage; Efficient energy use; Machine design; Energy (signal processing); Engineering; Mechanical engineering; Electrical engineering; Physics; Programming language","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0005113959,0.0008973877,0.0008641837,0.0005451327,0.0003399831,0.0007947713,0.0008443479,0.0008119692,0.003472674],"category_scores_gemma":[0.001136998,0.0005518516,0.0007900745,0.0004829915,0.0004113954,0.0006562175,0.0006636762,0.001076573,0.0005938352],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000752238,"about_ca_system_score_gemma":0.001246699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003990443,"about_ca_topic_score_gemma":0.004046219,"domain_scores_codex":[0.9997486,0.00006592135,0.00001082231,0.00005218868,0.00009316423,0.00002922064],"domain_scores_gemma":[0.9996221,0.0002247766,0.0000328908,0.00002910331,0.00007490278,0.00001610988],"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":[0.00001427338,0.00001827577,0.0002032311,0.00003997106,0.00001209982,0.00002111898,0.000009648793,0.9767426,0.0007448288,0.002468643,0.0002936702,0.01943165],"study_design_scores_gemma":[0.000001478477,0.000005442921,0.00001785481,0.000002087947,0.000001403242,0.00000223264,0.000001910729,0.9988918,0.0001432512,0.0006949904,0.0002367001,9.316918e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0173793,0.0004018691,0.975188,0.0002223484,0.00004497706,0.00006326368,0.00008376681,0.0005631531,0.00605324],"genre_scores_gemma":[0.6097811,0.0004138964,0.3827106,0.0001790193,0.00006300033,0.0003880588,0.0003172889,0.0001594353,0.005987692],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003990443,"threshold_uncertainty_score":0.0116173,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02056770999791476,"score_gpt":0.2383902478032379,"score_spread":0.2178225378053231,"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."}}