{"id":"W4286697509","doi":"10.4028/p-q6v323","title":"Performance Analysis of Work-Roll Wear Models on Hot Rolling","year":2022,"lang":"en","type":"article","venue":"Key engineering materials","topic":"Metal Alloys Wear and Properties","field":"Materials Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"ArcelorMittal (Canada)","funders":"ArcelorMittal","keywords":"Artificial neural network; Work (physics); Calibration; Amplitude; Inverse; Process (computing); Mechanical engineering; Computer science; Engineering; Materials science; Mathematics; Machine learning; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0008312227,0.0001602341,0.0004584266,0.0002845478,0.0001056888,0.00005408543,0.0003067283,0.00003252692,0.002860728],"category_scores_gemma":[0.00002417965,0.0001411331,0.00009297518,0.0005066079,0.00001756312,0.0001297549,0.0001486259,0.00007137007,0.00004893628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003917909,"about_ca_system_score_gemma":0.00001511588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003890278,"about_ca_topic_score_gemma":2.606998e-7,"domain_scores_codex":[0.9986646,0.00007738213,0.0003815712,0.0002416676,0.0003685146,0.0002663331],"domain_scores_gemma":[0.9994547,0.00004618396,0.000105627,0.0003192667,0.00002605606,0.00004818572],"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":[0.00008823344,0.00001362534,0.00002300567,0.00002850459,0.00005126406,9.242798e-7,0.0002216414,0.4919138,0.507477,0.0001473491,0.00001402199,0.00002058921],"study_design_scores_gemma":[0.0001705091,0.000137991,0.00102764,0.00003299372,0.0001915077,0.00000136343,0.00002680318,0.04943034,0.9482612,0.00001753677,0.0004654645,0.0002366524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998116,0.00008447417,0.0002858767,0.00001684411,0.0007677026,0.0001138884,0.00008432432,0.000102198,0.0004287217],"genre_scores_gemma":[0.9985757,0.00002057213,0.0009382896,0.00003064661,0.00005613962,0.00005523426,0.00001780162,0.00002644312,0.0002791464],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4424835,"threshold_uncertainty_score":0.9980508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02308641784102888,"score_gpt":0.1931914315637647,"score_spread":0.1701050137227358,"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."}}