{"id":"W4313426856","doi":"10.1002/fam.3123","title":"Bond behaviour of rebar in concrete at elevated temperatures: A soft computing approach","year":2022,"lang":"en","type":"article","venue":"Fire and Materials","topic":"Concrete Corrosion and Durability","field":"Engineering","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Rebar; Soft computing; Mean squared error; Regression; Regression analysis; Root mean square; Polynomial; Linear regression; Mathematics; Structural engineering; Polynomial regression; Applied mathematics; Statistics; Computer science; Biological system; Engineering; Artificial intelligence; Mathematical analysis; Artificial neural network","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":[0.0002922159,0.00009883896,0.0002491007,0.00003508769,0.00007144432,0.00001764461,0.00007010693,0.00004013494,0.0002479276],"category_scores_gemma":[0.00002020796,0.00009898285,0.00002211575,0.00009221933,0.00002249954,0.00002953811,0.0001443873,0.00007816689,0.000001275014],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004121214,"about_ca_system_score_gemma":0.000009853306,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008633533,"about_ca_topic_score_gemma":0.000002864801,"domain_scores_codex":[0.9992858,0.00005797806,0.0002738393,0.0001498078,0.00009055309,0.0001420821],"domain_scores_gemma":[0.9997439,0.0000344259,0.00003641927,0.0001382539,0.00001245645,0.00003457682],"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":[0.00005263402,0.000004489499,0.002676779,0.0001865341,0.000004630042,0.000007184149,0.0009466954,0.00009336002,0.9946973,0.00005123814,0.001181743,0.00009742386],"study_design_scores_gemma":[0.003139985,0.0002694809,0.03921968,0.0001430983,0.00005396912,0.0001370706,0.001797355,0.0135713,0.9343561,0.00009187,0.006285064,0.0009349996],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989631,0.0002580346,0.00001229042,0.00001077754,0.0002165976,0.0001811305,0.0001404197,0.00007638214,0.0001412324],"genre_scores_gemma":[0.9995863,0.00001593908,0.0001690722,0.00003329361,0.00001900924,0.00001848894,0.00008154124,0.00001457963,0.00006172328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06034117,"threshold_uncertainty_score":0.4036404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009789885660867153,"score_gpt":0.2059524291934591,"score_spread":0.1961625435325919,"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."}}