{"id":"W1997014250","doi":"10.1007/s11947-008-0110-x","title":"Wheat Class Identification Using Thermal Imaging","year":2008,"lang":"en","type":"article","venue":"Food and Bioprocess Technology","topic":"Spectroscopy and Chemometric Analyses","field":"Chemistry","cited_by":46,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada; University of Manitoba","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Drop (telecommunication); Moisture; Thermal; Materials science; Winter wheat; Water content; Horticulture; Analytical Chemistry (journal); Environmental science; Animal science; Meteorology; Agronomy; Composite material; Chemistry; Geography; Environmental chemistry; Biology; Geology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000145408,0.0002724101,0.0001914718,0.0007294227,0.000174517,0.0005303637,0.0001592628,0.0003001023,0.001070392],"category_scores_gemma":[0.0001984472,0.0001637733,0.0002253762,0.0005423405,0.0001475161,0.0004153738,0.0001777543,0.0003836893,0.0004134187],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000157303,"about_ca_system_score_gemma":0.000085403,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005669062,"about_ca_topic_score_gemma":0.00141386,"domain_scores_codex":[0.9998951,0.000009181569,0.000003757322,0.00005160633,0.0000260981,0.00001419484],"domain_scores_gemma":[0.9998741,0.00002811342,0.00003075058,0.00001304749,0.00003851954,0.00001548354],"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":[0.00007582334,0.0000113998,0.002523107,0.00002308581,0.000008190958,0.00001115853,0.00002852852,0.0001535548,0.9745768,0.00009600264,0.00008469675,0.02240762],"study_design_scores_gemma":[0.00001097005,0.0001601032,0.07577648,0.00001056892,0.00008232451,0.0003208025,0.0001381046,0.03279615,0.8871597,0.0003873871,0.00313413,0.00002328358],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8764094,0.0005638344,0.1168704,0.00005833179,0.00004176513,0.0000248683,0.0004281169,0.0004359737,0.005167435],"genre_scores_gemma":[0.9425109,0.000290094,0.05396792,0.00005040481,0.00002064951,0.00002457915,0.0002983712,0.00007006429,0.002766978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001070392,"threshold_uncertainty_score":0.003580749,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01813225177467248,"score_gpt":0.260678884008534,"score_spread":0.2425466322338615,"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."}}