{"id":"W3147725913","doi":"10.1007/s11694-021-00898-7","title":"Characterising corn grain using infrared imaging and spectroscopic techniques: a review","year":2021,"lang":"en","type":"review","venue":"Journal of Food Measurement & Characterization","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Hyperspectral imaging; Ranging; Grain quality; Environmental science; Materials science; Remote sensing; Agronomy; Computer science; Biology","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":[0.001262966,0.001448187,0.001652491,0.003290259,0.0002506669,0.001342559,0.0012421,0.001246411,0.001819716],"category_scores_gemma":[0.001135153,0.0005685266,0.0008006781,0.003494562,0.0006365909,0.002154466,0.0008503182,0.001344095,0.001318908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005073831,"about_ca_system_score_gemma":0.001253635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001451755,"about_ca_topic_score_gemma":0.002164087,"domain_scores_codex":[0.9996265,0.00003191453,0.00004586694,0.00009692019,0.0001702318,0.00002849571],"domain_scores_gemma":[0.999159,0.0003675766,0.0001395253,0.00002687108,0.0002607049,0.00004640719],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007037381,0.000126909,0.0003071648,0.01986038,0.0001187295,0.0001153413,0.00004269319,0.000537637,0.009674194,0.001875753,0.01030553,0.9569653],"study_design_scores_gemma":[0.00003372112,0.0003290747,0.002934003,0.004952947,0.0004907866,0.001460458,0.0001723356,0.0005836077,0.01066984,0.002409017,0.9758463,0.0001180411],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002496686,0.9984181,0.0005355955,0.0001160505,0.0001382575,0.000008055283,0.0000237499,0.000009431449,0.0005011208],"genre_scores_gemma":[0.0009051395,0.9974332,0.0008578434,0.0001248613,0.0001312324,0.000008967322,0.00004041779,0.000002818759,0.0004955408],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.003290259,"threshold_uncertainty_score":0.006679237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08088087618874153,"score_gpt":0.3377580070613545,"score_spread":0.256877130872613,"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."}}