{"id":"W4412911054","doi":"10.3390/polym17152032","title":"Energy-Based Approach for Fatigue Life Prediction of Additively Manufactured ABS/GNP Composites","year":2025,"lang":"en","type":"article","venue":"Polymers","topic":"Additive Manufacturing and 3D Printing Technologies","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Materials science; Composite material; Stress (linguistics); Range (aeronautics); Modulus; Acrylonitrile butadiene styrene; Composite number; Structural engineering; Fatigue limit; Stress concentration; Fracture mechanics","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":[0.00004933315,0.0001651375,0.0002075898,0.0002261613,0.00006726709,0.00001360819,0.0001794659,0.0001268703,0.0000262624],"category_scores_gemma":[0.00004108085,0.0001629122,0.00009870771,0.0001235235,0.00009520636,0.00003983398,0.00003043632,0.00009689534,5.497964e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003370587,"about_ca_system_score_gemma":0.00001977227,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002862979,"about_ca_topic_score_gemma":9.858321e-7,"domain_scores_codex":[0.9993147,0.00001002374,0.0002036399,0.0001812806,0.00008591791,0.0002043848],"domain_scores_gemma":[0.9995395,0.0001549623,0.00005072999,0.0001942419,0.00002715056,0.00003344723],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005073205,0.0005500417,0.003049222,0.003774236,0.00274741,0.000003884433,0.0006318665,0.318208,0.1025465,0.03076471,0.09049456,0.4467223],"study_design_scores_gemma":[0.0004568102,0.00003676719,0.002824099,0.00005851938,0.00004277722,1.995615e-7,0.0001162802,0.06820348,0.9237064,0.0004357253,0.003980622,0.0001383955],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02043835,0.0004810146,0.9671664,0.0001139975,0.0002661959,0.0002058841,0.0005995531,0.001173831,0.009554828],"genre_scores_gemma":[0.9893091,0.00001188889,0.01000269,0.00006296894,0.00003274167,0.0001055376,0.0002465407,0.00002172837,0.0002068249],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9688707,"threshold_uncertainty_score":0.6643369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0177214932474513,"score_gpt":0.2196818229277226,"score_spread":0.2019603296802713,"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."}}