{"id":"W4414016071","doi":"10.1016/j.porgcoat.2025.109634","title":"Enhancing powder coating classification accuracy via mesh-modified classifier design and operational parameter optimization","year":2025,"lang":"en","type":"article","venue":"Progress in Organic Coatings","topic":"Granular flow and fluidized beds","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Foshan Science and Technology Bureau; National Natural Science Foundation of China","keywords":"Materials science; Coating; Classifier (UML); Powder coating; Composite material; Biological system; Pattern recognition (psychology); Artificial intelligence; Computer science","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":[0.0004813318,0.0002167135,0.0002282382,0.0001962626,0.0001512043,0.0001789003,0.000145002,0.0001739523,0.00009770575],"category_scores_gemma":[0.0003396006,0.0002262542,0.00002967708,0.0004892736,0.00006851267,0.0003129437,0.00005970206,0.0002720866,0.000005876877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001200503,"about_ca_system_score_gemma":0.00005871641,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005347983,"about_ca_topic_score_gemma":0.000007191702,"domain_scores_codex":[0.9986264,0.00009256127,0.0004936091,0.0003184095,0.0001823721,0.0002866118],"domain_scores_gemma":[0.999177,0.0004083269,0.0000671624,0.0001950859,0.0001014944,0.00005092952],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001096772,0.0002104422,0.02829005,0.0006738795,0.0001471682,0.00001292878,0.004363908,0.02185043,0.793618,0.006161561,0.0005462353,0.1440157],"study_design_scores_gemma":[0.0007702039,0.00002140989,0.001618016,0.0001491219,0.00002959432,0.000005749759,0.0001197826,0.9083956,0.08819693,0.0003379096,0.00008826651,0.0002673803],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1290413,0.0008206176,0.8685203,0.0003289119,0.0002201472,0.00055599,0.000001743573,0.0002484607,0.0002625305],"genre_scores_gemma":[0.9439918,0.00006978207,0.05553031,0.000123562,0.00003665497,0.0001407532,0.00003508072,0.00003864757,0.00003345784],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8865452,"threshold_uncertainty_score":0.9226378,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01713147276013891,"score_gpt":0.2557176069356364,"score_spread":0.2385861341754975,"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."}}