{"id":"W1971332480","doi":"10.1007/s11694-010-9104-2","title":"Identification of wheat classes at different moisture levels using near-infrared hyperspectral images of bulk samples","year":2010,"lang":"en","type":"article","venue":"Sensing and Instrumentation for Food Quality and Safety","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":55,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"University of Manitoba","keywords":"Hyperspectral imaging; Linear discriminant analysis; Principal component analysis; Quadratic classifier; Moisture; Remote sensing; Mathematics; Near-infrared spectroscopy; Environmental science; Pattern recognition (psychology); Geography; Artificial intelligence; Statistics; Computer science; Support vector machine; Physics; Meteorology; Optics","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":[0.0001578594,0.0001297882,0.0002944547,0.00005582398,0.0002584043,0.00004242326,0.00004126674,0.0001098499,0.00003336066],"category_scores_gemma":[0.0001252237,0.0001197602,0.00007903841,0.0001012391,0.0002448397,0.0001145191,0.0000273439,0.00009770176,8.002635e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003680998,"about_ca_system_score_gemma":0.00001956997,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001153046,"about_ca_topic_score_gemma":0.00008875554,"domain_scores_codex":[0.9989768,0.00002337957,0.0005034059,0.0002213806,0.0001468512,0.0001281404],"domain_scores_gemma":[0.99918,0.0001725245,0.0003389459,0.000151612,0.000108364,0.0000485704],"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":[0.0001511915,0.00004090885,0.006141875,0.0003102902,0.0001008677,4.630232e-8,0.0006716039,0.000005441898,0.9876816,0.0007318745,0.000006877255,0.0041574],"study_design_scores_gemma":[0.0007364845,0.000053096,0.02871298,0.00002292721,0.0001517719,0.00000750483,0.002680749,0.0006580807,0.9652327,0.001587213,0.00002849603,0.0001279405],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.99722,0.0001373073,0.001678346,0.0001323019,0.00006436158,0.0000759018,0.0004909253,0.00001739905,0.0001834366],"genre_scores_gemma":[0.9960602,0.00006012234,0.003631318,0.00001670146,0.00003476566,7.390115e-7,0.00008683663,0.000009312536,0.00009999787],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02257111,"threshold_uncertainty_score":0.4883677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05717054522608948,"score_gpt":0.3337119666151768,"score_spread":0.2765414213890873,"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."}}