{"id":"W4388478735","doi":"10.1016/j.est.2023.109345","title":"Detailed experimentation and prediction of thermophysical properties in lauric acid-based nanocomposite phase change material using artificial neural network","year":2023,"lang":"en","type":"article","venue":"Journal of Energy Storage","topic":"Phase Change Materials Research","field":"Engineering","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Mean absolute percentage error; Materials science; Nanocomposite; Fourier transform infrared spectroscopy; Mean squared error; Viscosity; Analytical Chemistry (journal); Phase (matter); Scanning electron microscope; Lauric acid; Chemical engineering; Nanotechnology; Mathematics; Chemistry; Composite material; Chromatography; Organic chemistry; Statistics","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":[0.0002047332,0.0001076362,0.0002340423,0.0002535243,0.00003387903,0.00003795682,0.0000706355,0.00005324428,0.00001778443],"category_scores_gemma":[0.000007657439,0.00009357515,0.00003979276,0.0002675837,0.00003557737,0.0002536195,0.00002356725,0.00007207882,3.48314e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000810118,"about_ca_system_score_gemma":0.00001713671,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002103384,"about_ca_topic_score_gemma":0.00001176681,"domain_scores_codex":[0.9990534,0.000106723,0.0003528198,0.00007672293,0.0002154229,0.0001949264],"domain_scores_gemma":[0.9997144,0.00001737303,0.0001030643,0.00006573495,0.00004668526,0.00005278751],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004099526,0.00006552036,0.0000664627,0.00005222146,0.00001914213,0.00005200873,0.0003128345,0.05909536,0.9390111,0.000009165,0.00001418822,0.0008920394],"study_design_scores_gemma":[0.000802037,0.000206935,0.0008853362,0.00009232201,0.0000112552,0.000008329778,0.00004218223,0.322647,0.6752064,0.00002297536,0.00001254214,0.00006271557],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9985403,0.0002507572,0.000374745,0.00001550568,0.0006722567,0.00008903602,0.00002044148,0.00003235866,0.000004566034],"genre_scores_gemma":[0.9989179,0.00002854586,0.00009808395,0.00000775543,0.0008888905,0.00001723669,0.00001393607,0.00002636125,0.000001224131],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2638047,"threshold_uncertainty_score":0.3815884,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09661998242652338,"score_gpt":0.3013000581582787,"score_spread":0.2046800757317553,"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."}}