{"id":"W2324286875","doi":"10.1021/ie400104a","title":"Multivariate Image Regression for Quality Control of Natural Fiber Composites","year":2013,"lang":"en","type":"article","venue":"Industrial & Engineering Chemistry Research","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"","keywords":"Multivariate statistics; Materials science; Chemometrics; Composite material; Fiber; Partial least squares regression; Natural fiber; Filler (materials); Hyperspectral imaging; Computer science; Mathematics; Artificial intelligence; Statistics; Machine learning","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":[0.0006308838,0.0002479184,0.0005006265,0.0001062468,0.0001149075,0.00009032296,0.0004892587,0.0004054231,0.003356877],"category_scores_gemma":[0.0025614,0.0002183011,0.0001996908,0.0005190952,0.0001410559,0.0001448448,0.000110476,0.001039715,0.00003278867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001748168,"about_ca_system_score_gemma":0.00009413943,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003225849,"about_ca_topic_score_gemma":1.663999e-7,"domain_scores_codex":[0.9978244,0.00002988288,0.0005240686,0.0003967278,0.0005798661,0.0006450853],"domain_scores_gemma":[0.9968753,0.001763415,0.0001443646,0.0004612544,0.0005713113,0.0001843394],"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":[0.0001360127,0.00008945697,0.0003456858,0.0003531337,0.0001522915,0.000001617516,0.00002344218,0.0001086033,0.9967493,0.00001057453,0.001583332,0.0004464961],"study_design_scores_gemma":[0.002286505,0.00001847224,0.000109142,0.0001140357,0.00004037342,0.000002724496,0.00008321885,0.004999726,0.9907512,0.00002307133,0.001347065,0.0002244757],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9950222,0.0004529924,0.0001395543,0.0002648286,0.0000847465,0.0003383351,0.0001368729,0.0001300619,0.003430452],"genre_scores_gemma":[0.9921321,0.000005273723,0.0004099603,0.000003759327,0.0006296949,0.0001464582,0.00007786827,0.00003908646,0.006555754],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005998163,"threshold_uncertainty_score":0.9975542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08362866905457592,"score_gpt":0.3944249880954027,"score_spread":0.3107963190408267,"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."}}