{"id":"W2938945452","doi":"10.7451//cbe.2018.60.7.1","title":"Non-destructive and rapid discrimination of hard-to-cook beans using hyperspectral imaging","year":2018,"lang":"en","type":"article","venue":"Canadian Biosystems Engineering","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Hyperspectral imaging; Near-infrared spectroscopy; Relative humidity; Spectral imaging; Materials science; Remote sensing; Environmental science; Optics; Physics; Geology; Meteorology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"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":[0.00006437158,0.0001521455,0.0002186811,0.000448231,0.00007943057,0.0000354531,0.0001125369,0.00006083884,0.00008461093],"category_scores_gemma":[0.00005018597,0.0001691178,0.00004248325,0.0003995039,0.00004521885,0.0001027859,0.00001528152,0.00008224051,0.000003283619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004120001,"about_ca_system_score_gemma":0.00008863565,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01951934,"about_ca_topic_score_gemma":0.002753658,"domain_scores_codex":[0.9991529,0.000002653833,0.0001904883,0.0002310946,0.00009774116,0.0003251124],"domain_scores_gemma":[0.9993764,0.00001632224,0.00005134941,0.0001787131,0.0000826316,0.0002945735],"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":[0.00000299686,0.000003648883,0.007017283,0.0001926383,0.00006750376,0.000007362327,0.000843412,0.00007863407,0.9912675,0.0002743124,0.00005076639,0.0001939147],"study_design_scores_gemma":[0.0002540729,0.00002519347,0.004315895,0.0002419753,0.0001442727,0.00007574979,0.002398713,0.01898826,0.9725879,0.00000995721,0.0005708999,0.0003870874],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9957318,0.0004270701,0.001184914,0.00004386189,0.000131083,0.00006199617,0.00004324999,0.00003250728,0.002343491],"genre_scores_gemma":[0.9978318,0.000003881511,0.001776008,0.00002256683,0.0002732725,0.000003562446,0.000004841334,0.00002794406,0.00005618544],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01890963,"threshold_uncertainty_score":0.9870098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01094623768658293,"score_gpt":0.2215960590498268,"score_spread":0.2106498213632439,"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."}}