{"id":"W4286266835","doi":"10.1016/j.foodres.2022.111710","title":"A mapping approach to assess the evolution of pores during dehydration","year":2022,"lang":"en","type":"article","venue":"Food Research International","topic":"Food Drying and Modeling","field":"Agricultural and Biological Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Dehydration; Shrinkage; Porosity; Volume (thermodynamics); Biological system; Reduction (mathematics); Process (computing); Thermodynamics; Process engineering; Chemistry; Mechanics; Materials science; Mineralogy; Computer science; Mathematics; Physics; Geometry; Composite material; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001109706,0.00004101476,0.00004745254,0.00005820491,0.0005526372,0.00004486829,0.0004518109,0.00001595435,0.00008723917],"category_scores_gemma":[0.0001532279,0.00001638413,0.00004019946,0.0004323462,0.00002866003,0.00006191331,0.0003579038,0.0002089274,0.000003720512],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000178319,"about_ca_system_score_gemma":0.00002178007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003890502,"about_ca_topic_score_gemma":0.00007135998,"domain_scores_codex":[0.9985214,0.0001872744,0.0001336511,0.0001641336,0.0008253435,0.0001682103],"domain_scores_gemma":[0.9995842,0.0001161199,0.00003629609,0.00004294902,0.0001853711,0.00003506186],"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":[0.0001285421,0.0002576531,0.003135158,0.00001137775,0.00005508056,7.837396e-7,0.001318265,0.01955451,0.9475321,0.0214763,0.0009622324,0.005567991],"study_design_scores_gemma":[0.001143688,0.003645441,0.6269901,0.0001375395,0.00001127563,0.0001224376,0.09217172,0.1345805,0.04723267,0.01700751,0.07612234,0.000834844],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9925621,0.00004779668,0.0004161565,0.001925022,0.0001350217,0.0001735302,0.00003682889,0.00001751329,0.004686006],"genre_scores_gemma":[0.9991002,0.000001611278,0.0001868635,0.00002152464,0.0002075084,0.0001157656,0.00003810862,6.515917e-7,0.0003277471],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9002994,"threshold_uncertainty_score":0.4250494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2021737806659039,"score_gpt":0.3370097161402492,"score_spread":0.1348359354743453,"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."}}