{"id":"W4403073760","doi":"10.1002/cjce.25516","title":"A physics‐constrained hybrid residual neural network for the prediction of moisture content in a closed‐cycle drying system","year":2024,"lang":"en","type":"article","venue":"The Canadian Journal of Chemical Engineering","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Zhejiang Province Public Welfare Technology Application Research Project; National Natural Science Foundation of China","keywords":"Residual; Water content; Artificial neural network; Content (measure theory); Environmental science; Process engineering; Biological system; Mathematics; Computer science; Engineering; Artificial intelligence; Geotechnical engineering; Algorithm; Mathematical analysis; Biology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000500751,0.0005894181,0.0004906749,0.0002321437,0.0002664804,0.0004440882,0.0007119831,0.000775541,0.0007733992],"category_scores_gemma":[0.000692412,0.0002989612,0.0004128026,0.000211347,0.0002961956,0.0007032292,0.0004218901,0.0006382907,0.0001173878],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000560754,"about_ca_system_score_gemma":0.0006409623,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01402502,"about_ca_topic_score_gemma":0.007909996,"domain_scores_codex":[0.99984,0.00003051873,0.00001076409,0.00005722306,0.00004238804,0.00001902043],"domain_scores_gemma":[0.9997898,0.00009294647,0.00002477912,0.00001396462,0.00006825389,0.0000102478],"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":[0.0001161359,0.0000761633,0.0009981937,0.00004845701,0.00003454879,0.00005296129,0.00002783246,0.9555253,0.008098728,0.0006208077,0.000280382,0.03412054],"study_design_scores_gemma":[0.000001242182,0.00001024463,0.00006771854,7.31613e-7,0.000001717584,0.000001131295,7.177409e-7,0.9994428,0.0004045199,0.00004550437,0.00002249907,0.000001343652],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.233117,0.0005849696,0.7615214,0.0002576907,0.00007829568,0.0000598431,0.00009186663,0.001127554,0.003161429],"genre_scores_gemma":[0.9794112,0.00009861056,0.01885279,0.00004127284,0.000008319675,0.00004483326,0.00007311521,0.00001511413,0.001454743],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01402502,"threshold_uncertainty_score":0.02788681,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0112620980446001,"score_gpt":0.1908002867980977,"score_spread":0.1795381887534976,"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."}}