{"id":"W2172395050","doi":"10.13031/2013.21718","title":"MAGNETIC RESONANCE IMAGE ANALYSIS TO EXPLAIN MOISTURE MOVEMENT DURING WHEAT DRYING","year":2006,"lang":"en","type":"article","venue":"Transactions of the ASABE","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Research Council Canada; University of Manitoba","keywords":"Moisture; Water content; Mass transfer; Environmental science; Soil science; Chemistry; Materials science; Composite material; Chromatography; Geology; Geotechnical engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000126878,0.0003038771,0.0001595078,0.0002355604,0.00009260507,0.0002258836,0.0001936344,0.0003229792,0.0008505706],"category_scores_gemma":[0.0003323263,0.000158833,0.0002278718,0.000186384,0.000115422,0.0002814055,0.00009151756,0.0002077119,0.0001998659],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001813081,"about_ca_system_score_gemma":0.0001253432,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002195998,"about_ca_topic_score_gemma":0.002261711,"domain_scores_codex":[0.9999714,0.000003482289,0.000001443155,0.00000942644,0.000008919064,0.000005310004],"domain_scores_gemma":[0.9999362,0.00002168316,0.00001301571,0.000009536611,0.00001565187,0.000003913482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001840478,0.00006106216,0.005660637,0.0001390961,0.00004316173,0.0006217833,0.0001271776,0.1990027,0.7548576,0.001891277,0.000767797,0.03664368],"study_design_scores_gemma":[0.00001229139,0.00005521283,0.01352166,0.000003638594,0.00001781667,0.0002034463,0.00002189635,0.9422892,0.04198756,0.0004853294,0.001386446,0.00001560415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6812364,0.0004935434,0.3144293,0.0001127411,0.00004251334,0.00006120999,0.0002570976,0.0006446769,0.002722422],"genre_scores_gemma":[0.9653921,0.0002877547,0.0325706,0.00002970854,0.000007883196,0.00002893865,0.0001512326,0.00006636187,0.001465355],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002195998,"threshold_uncertainty_score":0.004366457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00849515073505732,"score_gpt":0.1973157157698681,"score_spread":0.1888205650348107,"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."}}