{"id":"W4407116126","doi":"10.1142/s0219876225500057","title":"Evaluating Microstructural Characteristics of Aluminum–Silicon Carbide Nanomixtures: Application of Spatio-Temporal Graph Convolutional Neural Networks for Enhanced Analysis","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational Methods","topic":"Machine Learning in Materials Science","field":"Materials Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Silicon carbide; Materials science; Convolutional neural network; Nano-; Aluminium; Graph; Silicon; Computer science; Metallurgy; Composite material; Artificial intelligence; Theoretical 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":[0.0034422,0.0001804945,0.0006217592,0.0006494714,0.00008804964,0.0000743595,0.0007940626,0.0000887127,0.000103342],"category_scores_gemma":[0.001412518,0.0001674836,0.0003484375,0.0005172912,0.0002696908,0.0002367984,0.0001068784,0.000149981,4.271056e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001107264,"about_ca_system_score_gemma":0.0002771246,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007855864,"about_ca_topic_score_gemma":0.000008314023,"domain_scores_codex":[0.9965297,0.0005947314,0.001657294,0.0002775346,0.0007614999,0.0001792373],"domain_scores_gemma":[0.9921589,0.001877155,0.002623894,0.0001556266,0.003128509,0.00005586132],"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":[0.0004515872,0.00004230567,0.005854748,0.00003320403,0.0002445878,4.597057e-7,0.0001096431,0.5966231,0.3848387,0.001898212,0.00001695351,0.0098865],"study_design_scores_gemma":[0.0008946073,0.0001574859,0.07997938,0.00006910548,0.0002886184,0.00001629916,0.00003325043,0.7986379,0.1054485,0.01427887,0.00005055181,0.0001455057],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4835357,0.00005988982,0.5149418,0.000119359,0.001129454,0.000125718,0.000070398,0.000006178748,0.00001143998],"genre_scores_gemma":[0.6209173,0.00000204908,0.3787251,0.00007007315,0.0001528577,0.000009791052,0.0001054648,0.000006337195,0.00001099088],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2793902,"threshold_uncertainty_score":0.6829785,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02075892336822135,"score_gpt":0.4133178263360126,"score_spread":0.3925589029677913,"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."}}