{"id":"W4415900060","doi":"10.1016/j.mtcomm.2025.114239","title":"Machine learning assessment of mechanical properties of oil palm shell concrete","year":2025,"lang":"en","type":"article","venue":"Materials Today Communications","topic":"Concrete and Cement Materials Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Artificial neural network; Palm oil; Predictive modelling; Software deployment; Compressive strength; Shell (structure); Random forest; Convolutional neural network; Sensitivity (control systems)","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":[0.0006084687,0.0001207465,0.0003552624,0.000114444,0.0000879456,0.0000396973,0.0006589215,0.00006962187,0.0004834061],"category_scores_gemma":[0.0000637076,0.000110925,0.0000466054,0.0001595084,0.0001066683,0.00006271057,0.0004731981,0.0001276228,0.00000736954],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000383925,"about_ca_system_score_gemma":0.00005964729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002078082,"about_ca_topic_score_gemma":0.00001431032,"domain_scores_codex":[0.9987299,0.0002758425,0.0005672713,0.000101541,0.0001558204,0.0001695853],"domain_scores_gemma":[0.9987815,0.00009382742,0.00007978721,0.0009032641,0.0001128826,0.00002872762],"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":[0.0000137468,0.000009713413,0.00009238998,0.0004480107,0.00007804558,1.549886e-7,0.00008378631,0.0001349001,0.9911759,0.007365075,0.00004530077,0.0005530104],"study_design_scores_gemma":[0.0003037867,0.00003663794,0.0001933882,0.000255507,0.00003328153,4.545635e-7,0.0000834695,0.007806293,0.9856497,0.00008094308,0.005454918,0.0001016184],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9828855,0.001533978,0.0002670228,0.000326093,0.000218271,0.0001848286,0.00008462125,0.0001327793,0.01436692],"genre_scores_gemma":[0.9947496,0.003223015,0.001428602,0.00001240402,0.00001138642,0.00008231942,0.00007658493,0.00001821955,0.0003978671],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01396905,"threshold_uncertainty_score":0.5292957,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03326097015906549,"score_gpt":0.2955577583953229,"score_spread":0.2622967882362575,"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."}}