{"id":"W2083768548","doi":"10.13031/2013.25093","title":"Protein and Oil Contents Determination in Wheat Using Near-infrared (NIR) Hyperspectral Imaging","year":2008,"lang":"en","type":"article","venue":"2008 Providence, Rhode Island, June 29 - July 2, 2008","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Partial least squares regression; Hyperspectral imaging; Absorbance; Near-infrared spectroscopy; Materials science; Chemistry; Remote sensing; Mathematics; Chromatography; Optics; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001957368,0.0005075038,0.0006689124,0.000439202,0.0004830198,0.000168374,0.0003767728,0.0002533959,0.0003805329],"category_scores_gemma":[0.0003622348,0.0005090215,0.0001525225,0.0009838103,0.0004386763,0.0008962181,0.0001186319,0.0005968499,0.00005143132],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003638517,"about_ca_system_score_gemma":0.0002854244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001036418,"about_ca_topic_score_gemma":0.0003111988,"domain_scores_codex":[0.9968728,0.00006371355,0.0007250875,0.0008612956,0.0005970226,0.0008801116],"domain_scores_gemma":[0.9987056,0.0001073239,0.0003051429,0.0004924765,0.0001757461,0.0002137558],"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":[0.0009432256,0.001172516,0.3298847,0.001023352,0.00025183,0.002606116,0.00508053,0.0002010429,0.6365677,0.00005961535,0.01088974,0.01131972],"study_design_scores_gemma":[0.01623436,0.0003004342,0.01564087,0.001876857,0.0007516084,0.005355416,0.002721709,0.1329654,0.8051202,0.001260343,0.01283373,0.004939065],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9824097,0.004737839,0.000552369,0.0002496096,0.0001200993,0.0002438718,0.00004769454,0.0001548702,0.01148393],"genre_scores_gemma":[0.9689661,0.0007332322,0.007803766,0.0001498413,0.000231802,0.00007989715,0.00004031364,0.00007789086,0.02191711],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3142438,"threshold_uncertainty_score":0.9997361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02425926277982752,"score_gpt":0.2686702913800802,"score_spread":0.2444110286002527,"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."}}