{"id":"W3114288514","doi":"","title":"Identification of the tensile law of UHPFRC Materials from bending tests by means of digital image correlation","year":2017,"lang":"en","type":"preprint","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Optical measurement and interference techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Digital image correlation; Ultimate tensile strength; Deflection (physics); Materials science; Structural engineering; Softening; Bending moment; Tensile testing; Beam (structure); Curvature; Composite material; Law; Engineering; Optics; Geometry; Mathematics; Physics","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":[0.002635472,0.0002142351,0.0004163048,0.00008903938,0.0001609225,0.0004695655,0.002964558,0.0001999439,0.00003025974],"category_scores_gemma":[0.001743393,0.0001850843,0.0001699378,0.0001498328,0.0004989257,0.0005788674,0.001738017,0.0002464215,0.000004786625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000534309,"about_ca_system_score_gemma":0.00009510943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000977386,"about_ca_topic_score_gemma":0.0001437937,"domain_scores_codex":[0.9967806,0.001076468,0.000924267,0.0004838325,0.0005661798,0.0001686403],"domain_scores_gemma":[0.9920846,0.0005879322,0.001995051,0.002848381,0.002431628,0.00005238485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000580589,0.0002767708,0.001493027,0.0001143319,0.00004416863,1.425372e-7,0.001701239,0.000004486713,0.8776935,0.1138616,0.000341213,0.004463682],"study_design_scores_gemma":[0.0001250364,9.420653e-7,0.00399943,0.001664864,0.00003145033,4.689178e-7,0.00001927119,0.004781893,0.9688644,0.02027519,0.00008114982,0.0001558941],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3195283,0.0003314036,0.6401222,0.002508368,0.0006599865,0.0008590326,0.0008530529,0.0001854516,0.03495221],"genre_scores_gemma":[0.9862335,0.00005948999,0.01304262,0.000007296663,0.00000805633,0.00001922518,0.0001721575,0.00001552813,0.0004421475],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6667051,"threshold_uncertainty_score":0.754752,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204341391401736,"score_gpt":0.246052291301395,"score_spread":0.2240088773873777,"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."}}