{"id":"W2135565303","doi":"10.1007/s00521-012-1233-6","title":"RETRACTED ARTICLE: ANN model to predict the effects of composition and heat treatment parameters on transformation start temperature of microalloyed steels","year":2012,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Microstructure and Mechanical Properties of Steels","field":"Engineering","cited_by":12,"is_retracted":true,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure; Université du Québec","funders":"","keywords":"Austenite; Transformation (genetics); Materials science; Microalloyed steel; Continuous cooling transformation; Artificial neural network; Phase (matter); Chemical composition; Metallurgy; Thermodynamics; Computer science; Microstructure; Artificial intelligence; Chemistry; Bainite; Physics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":{"nature":"Retraction","reason":"Duplication of/in Article;Compromised Peer Review;","date":"1/19/2021 0:00","openalex_flagged":true},"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008650095,0.0007572778,0.0006568747,0.0005511693,0.0004700233,0.0008178164,0.001286615,0.002080709,0.01444842],"category_scores_gemma":[0.005189452,0.0002770897,0.0008149077,0.0004491397,0.0002619982,0.0009739627,0.0005046365,0.001656304,0.00458176],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005562856,"about_ca_system_score_gemma":0.0007348403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009938122,"about_ca_topic_score_gemma":0.01449707,"domain_scores_codex":[0.9997336,0.00004152061,0.00002921858,0.0000562268,0.0001109958,0.00002850545],"domain_scores_gemma":[0.9976141,0.000590934,0.00006605418,0.0001671663,0.001477836,0.00008384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001357031,0.0002983774,0.007525066,0.0007141865,0.000350095,0.003233181,0.0001274883,0.1575645,0.0141919,0.002960125,0.5836061,0.2280721],"study_design_scores_gemma":[0.0001351149,0.0002471015,0.006180402,0.0001580415,0.0002672306,0.0007138856,0.00007867505,0.8711718,0.02833838,0.004958735,0.08764748,0.0001031972],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"editorial","genre_gemma":"empirical","genre_scores_codex":[0.2686343,0.01051564,0.2919144,0.07269967,0.2971172,0.0002612757,0.01354494,0.01146838,0.03384422],"genre_scores_gemma":[0.6092163,0.003867507,0.06367356,0.006487749,0.01617233,0.0001781178,0.01127505,0.001877794,0.2872516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01444842,"threshold_uncertainty_score":0.04833484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009522782858710315,"score_gpt":0.217046960484613,"score_spread":0.2075241776259027,"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."}}