{"id":"W2793181757","doi":"10.3390/s18010288","title":"Machine Learning and Infrared Thermography for Fiber Orientation Assessment on Randomly-Oriented Strands Parts","year":2018,"lang":"en","type":"article","venue":"Sensors","topic":"Thermography and Photoacoustic Techniques","field":"Engineering","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Fundação de Amparo à Pesquisa do Estado de Minas Gerais; Conselho Nacional de Desenvolvimento Científico e Tecnológico; Consortium de Recherche et d’innovation en Aérospatiale au Québec; Pratt and Whitney Canada; Fonds Québécois de la Recherche sur la Nature et les Technologies; Bombardier","keywords":"Thermography; Materials science; Fiber; Orientation (vector space); Aerospace; Nondestructive testing; Stiffness; Composite material; Infrared; Structural engineering; Computer science; Engineering; Optics; Aerospace engineering; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001494827,0.000143651,0.0001409229,0.0001303881,0.0001195496,0.00002360484,0.00003442308,0.00007029599,0.00009419941],"category_scores_gemma":[0.00001327213,0.000128254,0.00006786843,0.0001624859,0.00006202601,0.00003911521,0.000005410693,0.0001251154,0.000001031044],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001074954,"about_ca_system_score_gemma":0.000003676239,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006136884,"about_ca_topic_score_gemma":0.00000349662,"domain_scores_codex":[0.9993854,0.00003428995,0.0001459623,0.0001565288,0.00009890537,0.0001789471],"domain_scores_gemma":[0.9996539,0.0001205954,0.0000309883,0.00009912725,0.00004276514,0.00005266853],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.01173859,0.0009945706,0.0695428,0.002091851,0.004131065,0.00006599264,0.0382921,0.04520619,0.2006233,0.02370922,0.01468786,0.5889164],"study_design_scores_gemma":[0.02149302,0.005129608,0.05627545,0.0005092621,0.0005307876,0.0000284286,0.001364807,0.3618427,0.1466453,0.007613643,0.3960865,0.002480577],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9806163,0.00009056125,0.00928275,0.00001617345,0.0002267586,0.0005385134,0.0001566164,0.0006376387,0.00843464],"genre_scores_gemma":[0.9973674,0.00006015873,0.002079732,0.00002089212,0.00008996779,0.00005554295,0.00005291405,0.00003238698,0.0002409755],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5864359,"threshold_uncertainty_score":0.5230048,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006680054496430517,"score_gpt":0.2481589744096819,"score_spread":0.2414789199132513,"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."}}