{"id":"W4402738313","doi":"10.1016/j.compositesa.2024.108491","title":"The use of digital thread for reconstruction of local fiber orientation in a compression molded pin bracket via deep learning","year":2024,"lang":"en","type":"article","venue":"Composites Part A Applied Science and Manufacturing","topic":"Advanced machining processes and optimization","field":"Engineering","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"U.S. Department of Energy; National Nuclear Security Administration; Old Dominion University; National Science Foundation","keywords":"Materials science; Composite material; Thread (computing); Bracket; Structural engineering; Mechanical engineering; Engineering","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":[0.0001200675,0.00007343686,0.00009256737,0.00009032402,0.0001293673,0.0001219678,0.00006677148,0.00002405456,0.000001315957],"category_scores_gemma":[0.00001073037,0.00005754426,0.00001428098,0.0001502363,0.0001654083,0.0004807987,0.00003435801,0.00007811198,4.098786e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000264672,"about_ca_system_score_gemma":0.000009991784,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002169959,"about_ca_topic_score_gemma":0.000004008895,"domain_scores_codex":[0.9994209,0.000003021924,0.0001822489,0.0001484371,0.0001189664,0.0001264172],"domain_scores_gemma":[0.9996738,0.0001734894,0.00004032484,0.0000603448,0.00002881404,0.00002316601],"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":[0.00002355272,0.000003716923,0.00006223223,0.0001196508,0.00000364893,1.324875e-7,0.0003004187,0.6641963,0.01434466,0.0002275298,0.000002760666,0.3207154],"study_design_scores_gemma":[0.0001368667,0.00002275831,0.000239026,0.0001135534,0.000006275731,0.000004010191,0.0001411573,0.7614257,0.2361121,0.0008141423,0.0009100193,0.00007439927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.470062,0.0001947569,0.5293179,0.000006710836,0.00009823503,0.0001668841,0.000002066966,0.00004913137,0.0001023481],"genre_scores_gemma":[0.9949676,0.00006501182,0.004908473,0.000002022355,0.0000115754,0.00001859427,0.000008746581,0.000008552393,0.000009434157],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5249056,"threshold_uncertainty_score":0.2346587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01450884640643527,"score_gpt":0.2385401187742855,"score_spread":0.2240312723678502,"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."}}