{"id":"W4389949354","doi":"10.3390/modelling5010001","title":"Machine Learning-Assisted Characterization of Pore-Induced Variability in Mechanical Response of Additively Manufactured Components","year":2023,"lang":"en","type":"article","venue":"Modelling—International Open Access Journal of Modelling in Engineering Science","topic":"Additive Manufacturing Materials and Processes","field":"Engineering","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Porosity; Compression (physics); Volume fraction; Artificial neural network; Composite material; Characterization (materials science); Shear (geology); Plasticity; Stress (linguistics); Biological system; Computer science; Artificial intelligence; Nanotechnology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004091172,0.0002103518,0.0004977372,0.001160904,0.00003793892,0.0002054463,0.00185841,0.00008634414,0.00004261018],"category_scores_gemma":[0.000330033,0.0002072289,0.00006829888,0.0008811941,0.00005721293,0.00171342,0.0003132199,0.0003713438,0.000001019303],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002018368,"about_ca_system_score_gemma":0.0001125763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006669373,"about_ca_topic_score_gemma":0.000001530864,"domain_scores_codex":[0.9975384,0.00008198416,0.001148332,0.0002651225,0.000685418,0.0002807856],"domain_scores_gemma":[0.998553,0.000371103,0.0004703529,0.0001535662,0.0003692273,0.00008270866],"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":[0.0003218917,0.00005682062,0.0002165339,0.00008070396,0.00001918368,0.00001711216,0.000182811,0.8103188,0.1884567,0.0001005725,6.179638e-7,0.0002283244],"study_design_scores_gemma":[0.0004501856,0.00003858338,0.008143158,0.0003856567,0.000005587027,0.000008743645,0.000007012532,0.7872494,0.2031772,0.0003641489,0.00002742682,0.0001428479],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6463396,0.000007117124,0.3529997,0.00003317062,0.0004022782,0.0001174439,0.00004941995,0.00002340809,0.00002785199],"genre_scores_gemma":[0.9964651,0.0001665968,0.003236081,0.000004181379,0.00003688887,0.000008063975,0.0000433327,0.00003056095,0.00000913217],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3501256,"threshold_uncertainty_score":0.8450548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0661586916772684,"score_gpt":0.3107749787409335,"score_spread":0.2446162870636651,"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."}}