{"id":"W4405761933","doi":"10.2139/ssrn.5070217","title":"Ai-Driven Residual Strength Diagnostics of Composites Using Their Electrical Behavior Under Low-Stress Cyclic Loading","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"Smart Materials for Construction","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Composite material; Materials science; Residual stress; Cyclic stress; Residual strength; Residual; Stress (linguistics); Computer science; Algorithm","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001642843,0.0002840667,0.0001233405,0.0004319841,0.0001556592,0.0003402341,0.0002565525,0.0002987085,0.002411545],"category_scores_gemma":[0.0004378886,0.0001384386,0.000107525,0.0002933966,0.0003743259,0.0003194484,0.0001927574,0.0003717438,0.0004690621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001720888,"about_ca_system_score_gemma":0.0001142236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005769152,"about_ca_topic_score_gemma":0.00110454,"domain_scores_codex":[0.9998709,0.000008864865,0.000004368418,0.00003497636,0.00005739929,0.00002342335],"domain_scores_gemma":[0.9997037,0.00007657649,0.00006450497,0.00003595052,0.0000920581,0.00002729995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001573419,0.00002246153,0.001174422,0.00004968067,0.000005935117,0.00005458524,0.00007790922,0.0007115176,0.9920007,0.0001338028,0.0001441908,0.005467436],"study_design_scores_gemma":[0.000006689232,0.0001794864,0.01771632,0.000007698517,0.00001564606,0.0001152995,0.0001041506,0.01769183,0.9630765,0.0001738461,0.0008913492,0.00002127401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9910306,0.0002036917,0.005396395,0.00006056383,0.00003531923,0.000009876173,0.0003807018,0.0002612512,0.002621626],"genre_scores_gemma":[0.9979868,0.00006370943,0.0009429056,0.00001715446,0.000007356113,0.000006803169,0.0001038984,0.00002370376,0.0008475975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002411545,"threshold_uncertainty_score":0.008067429,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01081472837126206,"score_gpt":0.2530285838427358,"score_spread":0.2422138554714737,"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."}}