{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009132813,0.00008833747,0.0001275763,0.00002203285,0.0002878706,0.00002533726,0.0002181439,0.00003515297,0.0001274002],"category_scores_gemma":[0.000004272947,0.00003400052,0.0001894858,0.0006694835,0.00002486556,0.00005484038,0.0000118812,0.00007993585,0.000005343166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002437459,"about_ca_system_score_gemma":0.000002484382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002917846,"about_ca_topic_score_gemma":0.003897488,"domain_scores_codex":[0.9992644,0.00003094424,0.0001786995,0.0001802749,0.0001715788,0.0001741264],"domain_scores_gemma":[0.9997585,0.00002984878,0.0000337966,0.0001069785,0.0000367644,0.00003407659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00002485015,0.00007906615,0.001133784,0.000006055537,0.00002806393,5.303697e-7,0.0001313758,0.02206264,0.9620641,0.00002874437,0.00006328039,0.01437749],"study_design_scores_gemma":[0.000338344,0.0002083509,0.7102161,0.00007894675,0.0004418856,0.000002337599,0.000876138,0.002893872,0.2822157,0.0006986479,0.001628034,0.0004015814],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9962808,0.0003493617,0.001041713,0.001602058,0.00005295919,0.0001226915,0.00003273063,0.00003683638,0.000480805],"genre_scores_gemma":[0.9974152,0.00001293843,0.0006249105,0.00009567081,0.00004273377,0.00001630008,0.000003136193,9.250385e-7,0.001788186],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7090823,"threshold_uncertainty_score":0.441093,"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."}}