{"id":"W3022970201","doi":"10.5539/jmsr.v9n2p59","title":"Prediction of the Emissivity Curve at High Temperatures of Low Carbon Steel","year":2020,"lang":"en","type":"article","venue":"Journal of Materials Science Research","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Universidade Federal Fluminense; Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Emissivity; Materials science; Thermography; Thermocouple; Sigmoid function; Welding; Carbon steel; Atmospheric temperature range; Composite material; Infrared; Optics; Thermodynamics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003304093,0.00006012715,0.000186018,0.0001461318,0.00007621417,0.00003282217,0.0005398319,0.0000456638,0.0000421914],"category_scores_gemma":[0.000386985,0.00003747101,0.00004800655,0.0006640059,0.0004415971,0.000119766,0.0001075862,0.0002089588,1.179749e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005501951,"about_ca_system_score_gemma":0.0001194192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002407975,"about_ca_topic_score_gemma":8.039054e-7,"domain_scores_codex":[0.9983277,0.0001796715,0.0003332071,0.00007764127,0.0008978324,0.0001839058],"domain_scores_gemma":[0.9991634,0.0001125324,0.0001232487,0.0001531826,0.0003593048,0.00008825961],"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":[0.00007404255,0.00001311604,0.0004853019,0.0001708165,0.000007842852,0.000002051985,0.0003631629,0.0004865931,0.9980959,0.0000273293,0.0002237281,0.00005016267],"study_design_scores_gemma":[0.00009283825,0.0001512905,0.02210753,0.0001486377,0.000004507754,0.00001045742,0.0000642899,0.0002992205,0.9769351,0.0001336429,0.00002448128,0.00002796572],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990863,0.0001130485,0.00002813572,0.00009317524,0.0003200212,0.0001143556,0.0001292437,0.00001416957,0.0001015021],"genre_scores_gemma":[0.9996703,0.00009590885,0.0001071762,0.000003715768,0.000109444,0.000001597464,1.906057e-7,0.000006501128,0.000005174222],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02162223,"threshold_uncertainty_score":0.1627083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04089037492628263,"score_gpt":0.2882958270946248,"score_spread":0.2474054521683422,"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."}}