{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001542533,0.0007725898,0.000411199,0.0008466505,0.0001647133,0.0003837051,0.0003620973,0.0002756431,0.0005934637],"category_scores_gemma":[0.002211597,0.0002122295,0.0005605459,0.0006464266,0.0003087163,0.0004820419,0.0002882418,0.0006330274,0.0002650372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003786224,"about_ca_system_score_gemma":0.0003988466,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001596851,"about_ca_topic_score_gemma":0.001589939,"domain_scores_codex":[0.9993967,0.0001731669,0.00002849606,0.0001420677,0.0002270869,0.00003248655],"domain_scores_gemma":[0.999173,0.0003422379,0.0001980898,0.00006771297,0.0002031636,0.00001575848],"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":[0.000485064,0.0002300284,0.006499331,0.0002652951,0.0001650212,0.0000815098,0.0001271986,0.182312,0.4150975,0.002772434,0.0007877598,0.3911768],"study_design_scores_gemma":[0.00000926427,0.00009589746,0.005091555,0.000004791595,0.00002537208,0.00004292797,0.00001809336,0.8937638,0.0993662,0.000540251,0.001016725,0.00002501754],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1079513,0.0003015496,0.889326,0.00007815066,0.00001634664,0.00004279927,0.0001608803,0.001497677,0.0006253964],"genre_scores_gemma":[0.5915811,0.0004516374,0.4060265,0.00003973297,0.00002730554,0.0001044868,0.0005010602,0.0002244939,0.001043664],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001596851,"threshold_uncertainty_score":0.008157849,"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."}}