{"id":"W4402952649","doi":"10.48550/arxiv.2408.05850","title":"Machine learning for characterizing uncertain elastic properties of fused filament fabricated materials for topology optimization applications","year":2024,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Topology Optimization in Engineering","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Topology optimization; Protein filament; Materials science; Topology (electrical circuits); Computer science; Nanotechnology; Composite material; Structural engineering; Engineering; Finite element method; Electrical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001606056,0.0002934345,0.0004235022,0.0003343727,0.00008886297,0.00003381995,0.0002761323,0.0003128723,0.00004430296],"category_scores_gemma":[0.00007098516,0.0003469488,0.0001151643,0.000234357,0.00007978779,0.00006287634,0.0002049728,0.0002569415,0.000004998425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002653153,"about_ca_system_score_gemma":0.00005712895,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001672721,"about_ca_topic_score_gemma":0.000002768693,"domain_scores_codex":[0.9988294,0.00003437712,0.0003624847,0.000456263,0.00004212336,0.0002753854],"domain_scores_gemma":[0.9992133,0.0001036013,0.0001556252,0.0002819276,0.0001933312,0.00005220743],"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":[0.00004441979,0.00001826541,0.00001701516,0.001759715,0.0002028362,0.000001358631,0.0001217289,0.9747829,0.01762128,0.005359563,0.00002206876,0.00004886499],"study_design_scores_gemma":[0.0003712908,0.00003948621,0.000005447739,0.0001687144,0.0002183544,0.000001617781,0.00005937929,0.9831429,0.0143375,0.0008535634,0.0004993339,0.0003024863],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03351475,0.0002861356,0.9630641,0.00005323605,0.0006556665,0.00150017,0.0002562313,0.0006005702,0.00006910268],"genre_scores_gemma":[0.9905784,0.0002707041,0.007898742,0.000007727104,0.00009568891,0.0001698761,0.0005146851,0.00009844486,0.0003657444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9570636,"threshold_uncertainty_score":0.9998983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04216640976058184,"score_gpt":0.1858806107389396,"score_spread":0.1437142009783577,"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."}}